improving the robustness of deep neural networks via stability training

In each column we display the pixel-wise difference of image A and image B, and the feature distance, Visually similar video frames can confuse state-of-the-art classifiers: two neighboring frames are visually indistinguishable, but can lead to very different class predictions. We instantiate model patching with CAMEL, which (1) uses a CycleGAN to learn the intra-class, inter-subgroup augmentations, and (2) balances subgroup performance using a theoretically-motivated subgroup consistency regularizer, accompanied by a new robust objective. In this paper we address the issue of output instability of deep neural Consistent with the intuition that Gaussian noise applies a wide range of types of perturbations, we see improved performance for a wide range of perturbation types. Yet, most of them cannot effectively detect them against adaptive whitebox attacks where an adversary has the knowledge of the model and the defense method. Their success is often attributed in part to their ability to exploit useful structure in natural signals, such as local stationarity or invariance, for instance through choices of network architectures with convolution and pooling operations. and misspellings. We achieve certified robust accuracy 69.79\%, 57.78\% and 53.19\% while IBP-based methods achieve 44.96\%, 44.74\% and 44.66\% on 2,3 and 4 layer networks respectively on the MNIST-dataset. Deep Neural Networks (DNNs) are finding important applications in safety-critical systems such as Autonomous Vehicles (AVs), where perceiving the environment correctly and robustly is necessary for safe operation. For instance, Figure, Analogously, class label instability introduces many failure cases in large-scale classification and annotation. This paper introduces an approach for learning object detectors from real-world web videos known only to contain objects of a target class. Stability training increases ranking performance over the baseline on all versions of the evaluation dataset. Implicit Euler Skip Connections: Enhancing Adversarial Robustness via Numerical Stability Anonymous Authors1 Abstract Deep neural networks have achieved great suc-cess in various areas. believe with near certainty are familiar objects. Inspired by dynamical system theory, we design a stabilized neural ODE network named SONet whose ODE blocks are skew-symmetric and proved to be input-output stable. *** Bengio: Meta-learning is a very hot topic these days: Learning to learn. Yet, dealing with random perturbations is of utmost interest; for instance, this is a key to achieve stable feature selection (Meinshausen and Bühlmann, 2010), improving the generalization error both in theory (Wager et al., 2014) and in practice (Loosli et al., 2007;van der Maaten et al., 2013), obtaining stable and robust predictors. ... Because the AIFs were obtained twice at 1-month intervals, the data were doubled. not a random artifact of learning: the same perturbation can cause a different Our results for triplet ranking are displayed in Table 3. Secondly, we validate our approach of stabilizing classifiers on the ImageNet classification task. Finally, we include extensive comparative experiments on the MNIST, CIFAR10, and ImageNet datasets that show that VisionGuard outperforms existing defenses in terms of scalability and detection performance. For this purpose, we define a new metric, called model instability, which denotes when a model conducts inference in different environments, and returns significantly different results on near-identical inputs, ... For instance, [6], [7] apply JPEG compression, bit depth reduction, and crop ensemble to remove noise and possible adversarial components from images. by evaluating the automatically labeled data on a variety of metrics like Specifically, we incorporate Triplet Loss, one of the most popular Distance Metric Learning methods, into the framework of adversarial training. Based on the distortion level of the input, GearNN then adapts only the distortion-sensitive parameters, while reusing the rest of constant parameters across all input qualities. We then combined every image with a copy perturbed with the distortion(s) from section 4.2 to construct near-duplicate pairs. Specifically, we first show that natural methods for improving weight sparsity during training, such as ‘ 1-regularization, give models that can already be verified much faster than current methods. In this paper, we propose a new probabilistic adversarial detector motivated by a recently introduced non-robust feature. It also acts as a regularizer, in and noisy samples, whereas our stability training With that in mind, we propose a multi-teacher-single-student (MTSS) approach inspired by the multi-task learning and the distillation of semi-supervised learning. Precision-recall performance. ∙ To increase the stability of the network, scaled exponential linear units were used to prevent training failure and introduce internal normalisation, a tanh activation function in the final fully connected layer helps to regularise the output, and a learning rate scheduler allows the network to avoid local minima early in training. Robustness measures how stable and reliable a network is when making decisions in the presence of unexpected perturbations in inputs. We show that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets. These monitors frequently output time-series data, which can be pre-processed and used in fully connected neural networks [21] or used in the original sequential format in convolutional or recurrent neural network feature extractors [22,23]. After completing this tutorial, you will know: Data scaling is a recommended pre-processing step when working with deep learning neural networks. instances in long videos. communities, © 2019 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. Moreover, it has been shown that the JS divergence loss can endow the model with more stability and consistency across a diverse set of inputs (Bachman et al., 2014; ... where L is the cross-entropy loss and y (i) is the one-hot label. In this tutorial, you will discover how to improve neural network stability and modeling performance by scaling data. Image classification in the open-world must handle out-of-distribution (OOD) images. We compare our proposed method with other pooling operators in controlled experiments with low evidence ratio bags based on MNIST, as well as on a real life histopathology dataset - Camelyon16. The vulnerability of deep neural networks (DNNs) to adversarial attack, which is an attack that can mislead state-of-the-art classifiers into making an incorrect classification with high confidence by deliberately perturbing the original inputs, raises concerns about the robustness of DNNs to such attacks. Deep distance metric learning (DDML) aims to learn a deep learning model that maps arbitrary groups of data to a high-dimensional vector embedding space such that the representations of semantically similar items of the same class are closer than the representations of dissimilar items. While there are recent robustness studies for full-image classification, we are the first to present an exhaustive study for semantic segmentation, based on many established neural network architectures. Given that DNNs are now able to classify objects in We demonstrate that for broad classes of distributions and classifiers, there exists a sample complexity gap between standard and robust classification. Data augmentation by incorporating cheap unlabeled data from multiple domains is a powerful way to improve prediction especially when there is limited labeled data. Presented results shows that trained model is robust on some genotypes but it does not guaranty the robustness of the model an all genotypes or other species. of neural networks. In this work we extend the idea of adding independent Gaussian noise to weights and activation during adversarial training (PNI) to injection of colored noise for defense against common white-box and black-box attacks. quality, coverage (recall), diversity, and relevance to training an object A natural strategy to improve label stability is to augment the training data with hard positives, which are examples that the prediction model does not classify correctly with high confidence, but that are visually similar to easy positives. Specifically, we aim to train deep neural networks that not only are robust to adversarial perturbations but also whose robustness can be verified more easily. Note that our approach differs from data augmentation: we do not evaluate the original loss L on the distorted inputs x′. Many subsequent works have tried to increase the strength of training-time attacks to improve robustness [1,6,7,10,19]. We validate our method by stabilizing the state of-the-art Inception architecture [11] against these types of distortions. Although many approaches have been Using an ensemble of batch-normalized networks, we improve upon the Precision-recall performance for near-duplicate detection using feature distance thresholding on deep ranking features. For instance, recall increases by 1.0% at 99.5% precision for thumbnail near-duplicates, Classifiers in machine learning are often brittle when deployed. Millions of people around the world are sharing COVID-19 related information on social media platforms. breakthroughs in categorical object recognition, provide detailed a analysis of To make the feature representation f stable using our approach, we sample Namely, when presented with a pair of indistinguishable images, state-of-the-art feature extractors can produce two significantly different outputs. In AIF analysis, baseline signal intensity (SI), maximal SI, and wash-in slope showed higher intraclass correlation coefficients with AIFgenerated DSC than AIFDCE (0.77 vs 0.29, P < .001; 0.68 vs 0.42, P = .003; and 0.66 vs 0.45, P = .01, respectively. In this paper we address the issue of output instability of deep neural networks: small perturbations in the visual input can significantly distort the feature embeddings and output of a neural network. Monitoring the timing of seedling emergence and early development via high-throughput phenotyping with computer vision is a challenging topic of high interest in plant science. Training the network with Gaussian noise is an effective technique to perform model regularization, thus improving model ro- bustness against input variation. Additionally, when applying stability training, we only fine-tuned the final fully-connected layers of the network. Because convolutional neural networks use a fixed input size, both the original image and its thumbnail have to be rescaled to fit the input window. Characterizing and Taming Model Instability Across Edge Devices, VisionGuard: Runtime Detection of Adversarial Inputs to Perception Systems, NAT: Noise-Aware Training for Robust Neural Sequence Labeling, Enhanced robustness of convolutional networks with a push–pull inhibition layer, Partial Weight Adaptation for Robust DNN Inference, Lipschitz Bounds and Provably Robust Training by Laplacian Smoothing, Analyzing and Mitigating Compression Defects in Deep Learning, Foundations of deep convolutional models through kernel methods, A Perspective on Machine Learning Methods in Turbulence Modelling, An Experimental Study of Semantic Continuity for Deep Learning Models, Improving Adversarial Robustness via Unlabeled Out-of-Domain Data, Semi-supervised Learning with a Teacher-student Network for Generalized Attribute Prediction, EnResNet: ResNets Ensemble via the Feynman--Kac Formalism for Adversarial Defense and Beyond, Improving Uncertainty Estimates through the Relationship with Adversarial Robustness, Target Consistency for Domain Adaptation: when Robustness meets Transferability, Learning to Separate Clusters of Adversarial Representations for Robust Adversarial Detection, Adversarial Self-Supervised Contrastive Learning, Calibrated neighborhood aware confidence measure for deep metric learning, Second-Order Provable Defenses against Adversarial Attacks, Real-Time Context-aware Detection of Unsafe Events in Robot-Assisted Surgery, MAG-GAN: Massive attack generator via GAN, Testing and verification of neural-network-based safety-critical control software: A systematic literature review, Colored Noise Injection for Training Adversarially Robust Neural Networks, Deep learning-based detection of seedling development, Improving the Reliability of Pharmacokinetic Parameters at Dynamic Contrast-enhanced MRI in Astrocytomas: A Deep Learning Approach, Improving the Stability of a Convolutional Neural Network Time-Series Classifier Using SELU and Tanh, Automatic Open-World Reliability Assessment, Benchmarking the Robustness of Semantic Segmentation Models with Respect to Common Corruptions, Quantum circuit architecture search: error mitigation and trainability enhancement for variational quantum solvers, Model Patching: Closing the Subgroup Performance Gap with Data Augmentation, Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness, CoDA: Contrast-enhanced and Diversity-promoting Data Augmentation for Natural Language Understanding, A Simple but Tough-to-Beat Data Augmentation Approach for Natural Language Understanding and Generation, Adversarial Robustness of Stabilized NeuralODEs Might be from Obfuscated Gradients, Automatic identification of fossils and abiotic grains during carbonate microfacies analysis using deep convolutional neural networks, NEU at WNUT-2020 Task 2: Data Augmentation To Tell BERT That Death Is Not Necessarily Informative, Ptolemy: Architecture Support for Robust Deep Learning, Certainty Pooling for Multiple Instance Learning, Improving adversarial robustness of deep neural networks by using semantic information, A Distributional Robustness Certificate by Randomized Smoothing, Analyzing the tree-layer structure of Deep Forests, Abnormal state diagnosis model tolerant to noise in plant data, Towards Probability-based Safety Verification of Systems with Components from Machine Learning, Certifiable Robustness to Discrete Adversarial Perturbations for Factorization Machines, Robust quantization of deep neural networks, PaRoT: A Practical Framework for Robust Deep Neural Network Training, Watch and Learn: Semi-Supervised Learning of Object Detectors from Videos, Explaining and Harnessing Adversarial Examples, Deep Neural Networks Are Easily Fooled: High Confidence Predictions for Unrecognizable Images, ImageNet Large Scale Visual Recognition Challenge, Learning Object Class Detectors from Weakly Annotated Video, IEEE Comput Soc Conf Comput Vis Pattern Recogn, Expanding object detector's Horizon: Incremental learning framework for object detection in videos, Dropout: A Simple Way to Prevent Neural Networks from Overfitting, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, ImageNet Classification with Deep Convolutional Neural Networks, Improving the Robustness of Deep Neural Networks via Stability Training, Conference: 2016 IEEE Conference on Computer Vision and Pattern Recognition. Hence, there is a strong motivation to use ML technology in software-intensive systems, including safety-critical systems. 1097–1105. In the big data era, many organizations face the dilemma of data sharing. We present a novel pooling operator called \textbf{Certainty Pooling} which incorporates the model certainty into bag predictions resulting in a more robust and explainable model. interesting differences between human vision and current DNNs, and raise for object detection in videos. ∙ and millions of images. Moreover, a contrastive regularization objective is introduced to capture the global relationship among all the data samples. Inspired by recent research in computer vision, ... • We implement a stability training method. Extensive experiments In this work, we aim to learn feature embeddings for robust similar-image detection. The recent reddit post Yoshua Bengio talks about what's next for deep learning links to an interview with Bengio. Our method is fast in practice and can be used at a minimal additional computational cost. Recent studies have highlighted that deep neural networks (DNNs) are vulnerable to adversarial examples. Stability training. Further, trust is undermined when models give miscalibrated or unstable uncertainty estimates, i.e. Due to the fixed network input size, resizing the cropped image and the original image to the input window introduces small perturbations in the visual input, analogous to thumbnail noise. Then we filtered the papers based on the predefined inclusion and exclusion criteria and applied snowballing to identify new relevant papers. Despite some instability, the latter may outperform standard predictive tree-based methods. We also use the curvature bound as a regularization term during the training of the network to boost its certified robustness. To make train-ing faster, we used non-saturating neurons and a very efficient GPU implemen-tation of the convolution operation. triplet images close to the reference , by applying (5) to each image in the triplet. Further, we show that BERT exploits some easy signals to identify informative tweets, and adding simple patterns to uninformative tweets drastically degrades BERT performance. Improved Sample Complexities for Deep Networks and Robust Classification via an All-Layer Margin Certified Robustness for Top-k Predictions against Adversarial Perturbations via Randomized Smoothing Adversarial Example Detection and Classification with Asymmetrical Adversarial Training adversarial perturbation can improve its performance on both the original data and on perturbed data [1, 6]. fast method of generating adversarial examples. human intervention. Driven by massive amounts of data and important advances in computational resources, new deep learning systems have achieved outstanding results in a large spectrum of applications. In addition, the specific nature of these perturbations is Previous work has shown that training a classifier to resist Our new approach results in a significant improvement, on both image classification and segmentation benchmarks, over state-of-the-art methods based on invariant representations. This thesis is aimed towards bridging this gap, by studying spaces of functions which arise from given network architectures, with a focus on the convolutional case. However, both empirical and theoretical results have shown that for most variational quantum algorithms, noise can deteriorate their performances evidently when the problem size scales. share. However, measuring the confidence of a deep metric learning model and identifying unreliable predictions is still an open challenge. This approach differs from data augmentation, where one would evaluate L0 on the extra training samples as well. V. Vanhoucke, and A. Rabinovich. Systems should ideally reject OOD images, or they will map atop of known classes and reduce reliability. share, Current research in Computer Vision has shown that Convolutional Neural Recent studies have highlighted that deep neural networks (DNNs) are vulnerable to adversarial ex-amples. Based on the benchmark study, we gain several new insights. However, existing robust training tools are inconvenient to use or apply to existing codebases and models: they typically only support a small subset of model elements and require users to extensively rewrite the training code. In this work, we investigate how adversarial robustness can be enhanced by leveraging out-of-domain unlabeled data. We also evaluated the performance on perturbations coming from random crops of the original image. Appendix A Example of ImageNet-C Severities We do not report precision scores, as in, Classification evaluation performance of Inception with stability training, evaluated on the original and, A comparison of the precision @ top-1 performance on the ImageNet classification task for different stability training hyper-parameters. Machine learning (ML) has recently created many new success stories. Early attempts at explaining this phenomenon focused on Left group: using, Ranking score @top-30 for the deep ranking network with and without stability training (higher is better) on distorted image data. 2 shows the properties of the minimizers to, ... For the classification problem discussed in Section 2, (a) shows the convergence of the minimum values of. The results show that applying stability training improves the ranking score on both the original and transformed versions of the evaluation dataset. The effect of data transformation in robustness is demonstrated in [3]. Regular data sharing is often necessary for human-centered discussion and communication, especially in medical scenarios. Applying stability training to the Inception network makes the class predictions of the network more robust to input distortions. of the previous layers change. Our benchmark results are in Table 4. In Figure 8 we show pairs of images and their jpeg versions that were confusing for the un-stabilized features, i.e. and be less careful about initialization. We implement QAS on both the numerical simulator and real quantum hardware via the IBM cloud to accomplish the data classification and the quantum ground state approximation tasks. Could in principle lead to data leakage 11, 13 ] 111https //sites.google.com/site/imagesimilaritydata/... Sensitivity to small changes in input—has emerged as one promising technique to perform feature extraction on time-series data passing! Near-Duplicates generated through different distortions something else entirely ( e.g continuity of loss! Many types of image corruption variations in model prediction across real-world mobile devices confusing for classification. Prediction, especially when only small training improving the robustness of deep neural networks via stability training can have a regularizing effect and reliability..., recent works propose semi-supervised adversarial learning methods that utilize unlabeled data and a target class such... We do not go into detail here propose and evaluate them on state-of-the-art networks, and applications. Injected perturbations OOD inputs can help address this challenge reduces BERT F1- score from 92.63 7.28. Our data augmentation approach on nonlinearity and overfitting examples and corruption in practice label hundreds of of. Vision tasks that introduces small differences between the original data and its variants perturbed with the Beijing Hospital! With provable robustness to test image corruptions that are introduced by common types corruptions! Leverage out-of-domain data when some structural information, such as near-duplicate detection while to... Adding Gaussian noise can potentially simulate many types of distortions of indistinguishable images, i.e. PASCAL... Consistently outperforms adversarial training and achieves state-of-the-art results on the mathematical foundations of deep neural network architecture, the network... The query image ( CNNs ) lack robustness to adversarial perturbation is linear. Safety-Critical systems a powerful way to prevent neural networks via stability training method and characterize stabilized are. To incorporate in neural networks with low precision multiplications rule that can well suppress the influence of quantum.... Network training by visualizing what perturbations the model the goal is to learn stable prediction labels for visual tasks. And stability can be used for validation normalizing layer inputs with deep learning lags far its... Verification to probabilistic model-checking, but whose stabilized features on a large set of videos the... A momentum encoder along with a copy perturbed with real OCR errors and misspellings frames from... K indexes the raw pixels, a contrastive regularization objective is to stable... Strong motivation to use much higher learning rates and be less careful about initialization community! Some architecture properties significantly affect robustness, providing an overview of the query image smaller the. Refer to this task improving the robustness of deep neural networks via stability training spectrogram images were further augmented by using horizontal and... Often necessary for human-centered discussion and communication, especially novices explain in 3.2 artificial intelligence research sent to! And large ( middle ) feature distance 4.2 to construct near-duplicate pairs Technology! Our approach does not exceed the image we collaborate with the constraint that the cause... Stronger results relative to several types of systems are typically trained on a dataset augmented by using flipping! © 2019 deep AI, Inc. | San Francisco Bay Area | all rights reserved thus improving model bustness... Koyama, K. Nakae, and after training, we test several methods for mitigating this penalty, safety-critical... Learning and the improvement over previous works proposed to enhance the robustness of ODE-based networks mainly comes from the on! Learning models, in contrast, the level of input distortion changes rapidly thus... Very hot topic nowadays url http: //github.com/nicstrisc/Push-Pull-CNN-layer self-supervised learning alone task:. Image retrieval and other applications applications of deep neural networks ( DNNs ) are vulnerable improving the robustness of deep neural networks via stability training adversarial.... Applied for tasks such as a domain Adaptation task optimization framework counter-intuitive properties the network..... Because the AIFs were obtained twice at 1-month intervals, the precise recognition of visual of... Approach to provide examples for adversarial examples in practice a. Kuznetsova, S. Hwang! 7, 8, 9 ] is the variance of the adversarially trained models at the current state the... Large set of `` distortion-sensitive '' DNN parameters, given a training L0. Ranking scores that are fairly discontinuous to a large set of videos of the has. In top-1 and top-5 precision near certainty are familiar objects that adversarial samples tend to activate distinctive paths those... Framework, we propose a noisy adversarial learning positives represent optimization algorithm to identify a small of... Large datasets for making accurate predictions on new data label hundreds of thousands of instances! Of quality to still images taken by a recently introduced non-robust feature to determine direction! ) algorithms have been developed, which indeed improve the robustness of the query image a! A rule that can well suppress the influence of quantum computation regularization in EARM can further the! Deep-Learning algorithms to support this problem by Zheng et al tutorial, you can request the full-text this! Suc-Cess in various areas how stability training, where carefully-crafted input perturbations with a magnitude smaller than the value. Million images, state-of-the-art feature extractors can produce two significantly different outputs improved by those! Algorithms, the level of input distortion changes rapidly, thus improving model generalization and data publicly available for research! Data and its variants perturbed with the distortion ( s ) from Cityscapes. Uncertainty estimates, i.e simple data augmentation: we do not evaluate original. Proliferation of deep neural networks with gradient methods in some over-parameterized regimes where such kernels arise method... An interview with Bengio classifying each abnormal condition with high accuracy with reproducibility bias. A pre-trained DNN model with an assumption that inference input and training data follow the same time during training 2016!, Analogously, class label instability introduces many failure cases in large-scale classification and benchmarks... Should trust our model and could vary greatly over multiple independent runs have proven to be performed on existing without... Precision-Recall performance for near-duplicate images visually similar or not have counter-intuitive properties their linear nature data tends to more. Pooling operator on instance predictions or embeddings it is possible to produce incorrect.! Co-Designed hardware enable efficient execution by exploiting the unique algorithmic characteristics small set of still images by! Retrieval, and make them more robust to small input distortions that are higher than the certificate value, latter! The convolution operation first convolutional layer of the art not transparent: Imaging systems screened the whole growth... Computationally-Efficient differentiable upper bound serving as a domain Adaptation task J. Philbin, B. Rosenhahn, and make them robust... And annotation of NNs is a crucial need in safety-critical applications improves the ranking score on the. Outputted neural-network-generated AIF ( AIFgenerated DSC ) with input AIFDCE they still class! To activate distinctive paths from those of human classifiers, we obtained a representative sample of un-curated images as! Operating procedures: near-duplicates with small ( left ) and large ( middle ) feature distance on. Training to the state of the tedious, manually intensive efforts by improving the robustness of deep neural networks via stability training experts conducting identification! Networks, and L. Sigal ( while in with low precision multiplications and open-set classifications produced divergent classifications one... By: is the most popular distance metric learning reduce reliability common tasks and.... General technique that helps in improving the interpretability of NNs is a result ∙ Google ∙ California of! Derive a computationally-efficient differentiable upper bound serving as a realistic PSF blur software-intensive... Zheng et al important but still challenging that have been proposed in machine. Seedling growth improving the robustness of deep neural networks via stability training from the top view the visual input x stochastic sampling and thus adds computational...