Popular as it is, representation learning raises concerns about the robustness of learned representations under adversarial … These advantages include better-behaved gradients (see Figure 3), representation invertibility, and more specialized features. Hadi Salman Research Engineer. VGG remains a mystery Although this experiment started because of an observation about a special characteristic of VGG nets, it did not provide an explanation for this phenomenon. • Adversarial Robustness as a Prior for Learned Representations An important goal in deep learning is to learn versatile, high-level feature representations of input data. It has made impressive applications such as pre-trained language models (e.g., BERT and GPT-3). 3 Qualitative Analysis of Latent Representations under Adversarial Attack We begin our investigation by analyzing how the adversarial images are represented by different models. Adversarial Robustness as a Feature Prior Unfortunately, we don’t have a way to explicitly control which features models learn (or in what way they learn them). Deep networks are well-known to be fragile to adversarial attacks. (All models are available for download via our code/model release, and more details on our training procedure can be found there and in our paper.) However, standard networks' representations seem to possess shortcomings that, as we illustrate, prevent them from … In practical machine learning, it is desirable to be able to transfer learned knowledge from some “source” task to downstream “target” tasks. Brandon Tran To sum up, we have two options of pretrained models to use for transfer learning. Step 3: Representation refnement To address the is-sue that adversarial training alone does not work well, we develop a novel technique that: (i) learns which parts of the input program are relevant for the given prediction, and (ii) refnes the model representation such … An interesting implication is that these artifacts, while problematic, seem orthogonal to the problem that adversarial robustness solves in neural style transfer. We We then transferred each model (using both the fixed-feature and full-network settings) to 12 downstream classification tasks and evaluated the performance. 3 Jun 2019 We find that adversarially robust source models almost always outperform their standard counterparts in terms of accuracy on the target task. Shibani Santurkar We also uncover a few somewhat mysterious properties: for example, resizing images seems to have a non-trivial effect on the relationship between robustness and downstream accuracy. Set task=train-classifier to test the classification accuracy of learned representations. We then re-cast robust optimization as a tool for enforcing human priors on the features learned … Adversarial Robustness: Adversarial training improves models’ robustness against attacks, where the training data is augmented using adversarial samples [17, 35]. • Unlike the widely used normal distribution assump-tion, we innovatively estimate structure-aware prior distribution of latent representation by bridging the graph and feature spaces with learned • to this paper, Deep Residual Learning for Image Recognition. I am broadly interested…, Programming languages & software engineering, “Do Adversarially Robust ImageNet Models Transfer Better?”, Transfer Learning using Adversarially Robust ImageNet models, AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles. Then, we applied the transferring strategy in an adversarial manner to generalize the learned representations to the sample-scarce area. By add a task Researchers can use the These are properties that are fundamental to any “truly human-level” representation. We conduct an empirical analysis of deep representations under the state-of-the-art attack method called PGD, and find that the attack causes the internal representation to shift closer to the "false" class. We hope that our work paves the way for more research initiatives to explore and understand what makes transfer learning work well. Another work shows that adversarially trained learn robust feature extractors that can be … In addition to improving robustness to adversarial attacks, when used with the Euclidean norm, adversarial training imposes a prior that is closely aligned with human visual perception, resulting in trained networks with more interpretable feature representations [22, 23, 24]. These works have found that although these adversarially robust models tend to attain lower accuracies than their standardly trained counterparts, their learned feature representations carry several advantages over those of standard models. Transfer learning is very useful in practice. Code for "Learning Perceptually-Aligned Representations via Adversarial Robustness" These are notebooks for reproducing our paper "Learning Perceptually-Aligned Representations via Adversarial Robustness" (preprint, blog). The performance of the pretrained model on the source tasks plays a major role in determining how well it transfers to the source tasks. These desirable properties might suggest that robust neural networks are learning better feature representations than standard networks, which could improve the transferability of those features. It requires a larger network capacity than standard training [ ] , so designing network architectures having a high capacity to handle the difficult adversarial … regularization for better adversarial robustness. Adversarial robustness has been initially studied solely through the lens of machine learning security, but recently a line of work studied the effect of imposing adversarial robustness as a prior on learned feature representations. In particular, these representations are approximately invertible, while allowing for direct visualization and manipulation of salient input features. In our paper, we study this phenomenon in more detail. training alone fails to produce robust models of code. Running the notebooks. By carefully sampling examples for metric learning, our learned representation not only increases robustness, but also detects previously unseen adversarial samples. Quantitative experiments show improvement of robustness accuracy by up to 4% and detection efficiency by up to 6% according to Area Under Curve score over prior … Title: Adversarial Robustness as a Prior for Learned Representations Authors: Logan Engstrom , Andrew Ilyas , Shibani Santurkar , Dimitris Tsipras , Brandon Tran , Aleksander Madry (Submitted on 3 Jun 2019 ( v1 ), last revised 27 Sep 2019 (this version, v2)) While progress has been made in defending against imperceptible attacks, it remains unclear how patch-based attacks can be resisted. There, we analyze the effects of model width and robustness levels on the transfer performance, and we compare adversarial robustness to other notions of robustness. As we can see, adversarially robust models improve on the performance of their standard counterparts per architecture too, and the gap tends to increase as the network’s width increases: We also evaluate transfer learning on other downstream tasks including object detection and instance segmentation, both for which using robustness backbone models outperforms using standard models as shown in the table below: Overall, we have seen that adversarially robust models, although being less accurate on the source task than standard-trained models, can improve transfer learning on a wide range of downstream tasks. EXPLOITING EXCESSIVE INVARIANCE CAUSED BY NORM-BOUNDED ADVERSARIAL ROBUSTNESS Jorn-Henrik Jacobsen¨ Vector Institute and University of Toronto Jens In fact, a recent study by Kornblith, Shlens, and Le finds that a higher accuracy of pretrained ImageNet models leads to better performance on a wide range of downstream classification tasks. The question that we would like to answer here is whether improving the ImageNet accuracy of the pretrained model is the only way to improve its transfer learning. , Less surprisingly, we The hyperparameter \(\varepsilon\) governs the intended degree of invariance to the corresponding perturbations. In combination with adversarial training, later works [ 21 , 36 , 61 , 55 ] achieve improved robustness by regularizing the feature representations with additional loss, which can be viewed as adding additional tasks. Lectures from Microsoft researchers with live Q&A and on-demand viewing. For example, there have been several studies of the priors imposed by architectures (such as convolutional layers), loss functions, and data augmentation on network training. Finally, our work provides evidence that adversarially robust perception models transfer better, yet understanding precisely what causes this remains an open question. Do Adversarially Robust ImageNet Models Transfer Better? Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Brandon Tran, Aleksander Madry: Adversarial Robustness as a Prior for Learned Representations. Adversarial robustness has been initially studied solely through the lens of machine learning security, but recently a line of work studied the effect of imposing adversarial robustness as a prior on learned feature representations. By carefully sampling examples for metric learning, our learned representation not only increases robustness, but also detects previously unseen adversarial samples. For example, transfer learning allows perception models on a robot or other autonomous system to be trained on a synthetic dataset generated via a high-fidelity simulator, such as AirSim, and then refined on a small dataset collected in the real world. In our work we focus on computer vision and consider a standard transfer learning pipeline: “ImageNet pretraining.” This pipeline trains a deep neural network on ImageNet, then tweaks this pretrained model for another target task, ranging from image classification of smaller datasets to more complex tasks like object detection and image segmentation. While adaptive attacks designed for a particular defense are a way out of this, there are only approximate guidelines on how to perform them. Adversarial robustness as a prior for better transfer learning Learning web search intent representations from massive web search logs Reliability in Reinforcement Learning Getting into a conversational groove: New approach Adversarial machine learning and instrumental variables for flexible causal modeling, Newly discovered principle reveals how adversarial training can perform robust deep learning, Are all samples created equal? The Adversarial Robustness Toolbox is designed to support researchers and developers in creating novel defense techniques, as well as in deploying practical defenses of real-world AI systems. To answer this question, we trained a large number of standard and robust ImageNet models. (read more). One can thus view adversarial robustness as a very potent prior for obtaining representations that are more aligned with human perception beyond the standard goals of security and reliability. I am a research engineer in the Autonomous Systems Group working on robustness in deep learning. Adversarial robustness as a prior for learned representations, 2019. Learning Perceptually-Aligned Representations via Adversarial Robustness 06/03/2019 ∙ by Logan Engstrom, et al. In our work we focus on two common methods: The full-network transfer setting typically outperforms the fixed-feature strategy in practice. Reinforcement Based Learning on Classification Task Could Yield Better Generalization and Adversarial Accuracy 12/08/2020 ∙ by Shashi Kant Gupta, et … Steps to … This is known as transfer learning—a simple and efficient way to obtain performant machine learning models, especially when there is little training data or compute available for solving the target task. bidirectional adversarial learning. Which models are better for transfer learning? ∙ MIT ∙ 0 ∙ share This week in AI Get the week's most popular data science and artificial intelligence Adversarial Robustness as a Prior for Learned Representations Logan Engstrom*, Andrew Ilyas*, Shibani Santurkar*, Dimitris Tsipras*, Brandon Tran*, Aleksander Madry (2019) Blog Post, Github Adversarial Examples are not Get the latest machine learning methods with code. Optional commands:--classifier-loss = robust (adversarial classification) / standard (standard classification)--classifier-arch = mlp (mlp aslinear To add evaluation results you first need to. Read Paper                     Code & Models. Note that setting \(\varepsilon=0\) corresponds to standard training, while increasing ε induces robustness to increasingly large perturbations. Transfer learning is also common in many computer vision tasks, including image classification and object detection, in which a model uses some pretrained representation as an “initialization” to learn a more useful representation for the specific task in hand. By carefully sampling examples for metric learning, our learned representation not only increases robustness, but also detects previously unseen adversarial samples. Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Brandon Tran, Aleksander Madry: Adversarial Robustness as a Prior for Learned Representations. With the rapid development of deep learning and the explosive growth of unlabeled data, representation learning is becoming increasingly important. Accepted at the ICLR 2019 SafeML Workshop. [11] shows that robustness from adversarial training can be im-proved if the models are pre-trained from tasks from other domains. Browse our catalogue of tasks and access state-of-the-art solutions. Although many notions of robustness and reliability exist, one particular topic in this area that has raised a great deal of interest in recent years is that of adversarial robustness: can we develop cl… Our code and models for reproducing these results is available at https://git.io/robust-reps . CoRR abs/1906.00945 (2019) CoRR abs/1906.00945 (2019) Robustness Transfer There is a line of work that shows robustness can transfer from one model to another. Experimental results and analyses using data in different regions have revealed that the In this work, we study two different approaches for defending against black-box patch attacks. Many applications of machine learning require models that are human-aligned, i.e., that make decisions based on human-meaningful information about the input. • It is well known by now that standard neural networks are extremely vulnerable to such adversarial examples. Ultimately, the quality of learned features stems from the priors we impose on them during training. Logan Engstrom Adversarial robustness as a prior for learned representations, 2019. • In particular, these representations are approximately invertible, while allowing for direct visualization and manipulation of salient input features. In our paper “Do Adversarially Robust ImageNet Models Transfer Better?” we study another prior: adversarial robustness, which refers to a model’s invariance to small imperceptible perturbations of its inputs, namely adversarial examples. • More broadly, our results indicate adversarial robustness as a promising avenue for improving learned representations. Aleksander Madry, An important goal in deep learning is to learn versatile, high-level feature representations of input data. Adversarial training [ ] [ ] shows good adversarial robustness in the white-box setting and has been used as the foundation for defense. In this work, we show that robust optimization can be re-cast as a tool for enforcing priors on the features learned by deep neural networks. Title: Adversarial Robustness as a Prior for Learned Representations Authors: Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Brandon Tran, Aleksander Madry Download PDF Abstract: An important goal in. What it Thinks is Important is Important: Robustness Transfers through Input Gradients Alvin Chan1 , Yi Tay 1, Yew-Soon Ong1,2 1Nanyang Technological University, 2 AI3, A STAR, Singapore Abstract Adversarial perturbations As we seek to deploy machine learning systems not only on virtual domains, but also in real systems, it becomes critical that we examine not only whether the systems don’t simply work “most of the time”, but which are truly robust and reliable. We identify the pervasive brittleness of deep networks' learned representations as a fundamental barrier to attaining this goal. For instance, Figure 2 shows that a tiny perturbation (or change) of the pig image, a pretrained ImageNet classifier will mistakenly predict it as an “airliner” with very high confidence: Adversarial robustness is therefore typically enforced by replacing the standard loss objective with a robust optimization objective: This objective trains models to be robust to worse-case image perturbations within an \(\ell_2\) ball around the input. Editor’s note: This post and its research are the collaborative efforts of our team, which includes Andrew Ilyas (PhD Student, MIT), Logan Engstrom (PhD Student, MIT), Aleksander Mądry (Professor at MIT), Ashish Kapoor (Partner Research Manager). : Boosting generative models via importance weighting, Provable guarantees come to the rescue to break attack-defense cycle in adversarial machine learning. Adversarial performance of data augmentation and adversarial training This next table summarizes the adversarial performance, where adversarial robustness is with respect to the learned perturbation set. Andrew Ilyas More broadly, our results indicate adversarial robustness as a promising avenue for : Finally, we show that the representations learned through BYORL transfer much better to downstream tasks (i.e., downscaled S TL -10 (Coates et al., 2011) and C IFAR -100 (Krizhevsky et al., 2014)) than those obtained through pseudo-labeling and standard adversarial training. We can, however, disincentivize models from using features that humans definitely don’t use by imposing a prior … 7, 12, 16 Imagenet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness Jan 2019 Many applications of machine learning require models that are human-aligned, i.e., that make decisions based on human-meaningful information about the input. Moreover, adaptive evaluations are highly customized for particular models, which makes it difficult to compare different defenses. Adversarial robustness has been initially studied solely through the lens of machine learning security, but recently a line of work studied the effect of imposing adversarial robustness as a prior on learned feature representations ^ Adversarial Robustness as a Prior for Learned Representations, arXiv, 2019 ^ DROCC: Deep Robust One-Class Classification, ICML 2020 ^ ARAE: Adversarially Robust Training of Autoencoders Improves Novelty Detection, arXiv, 2020 After all, our goal is to learn broadly applicable features on the source dataset that can transfer to target datasets. CoRR abs/1906.00945 (2019) CoRR abs/1906.00945 (2019) Refining the ImageNet pretrained model can be done in several ways. Quantitative experiments show improvement of robustness In particular, these representations are approximately invertible, while allowing for direct visualization and manipulation of salient input features. In this work, we show that robust optimization can be re-cast as a tool for enforcing priors on the features learned by deep neural networks. Our code and models for reproducing these results is available at https://git.io/robust-reps . Quantitative experiments show improvement of robustness accuracy by up to 4% and detection efficiency by up to 6% according to Area Under Curve score over prior … More broadly, the results we observe indicate that we still do not yet fully understand (even empirically) the ingredients that make transfer learning successful. The right-hand side shows CIFAR-10 images closest (in representation space using cosine similarity) to the query image on the left. Finally, Chapter 5 returns to some of the bigger picture questions from this Chapter, and more: here we discuss the value of adversarial robustness beyond the typical “security” justifications; instead, we consider advesarial robustness in the context of regularization, generalization, and the meaningfulness of the learned representations. Patch-based adversarial attacks introduce a perceptible but localized change to the input that induces misclassification. Dimitris Tsipras 7, 12, 16 Imagenet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness Jan 2019 We use a non-robust self-supervised learning technique to learn image representations (i.e., BYOL; Grill et al., 2020). Improving the Robustness of Wasserstein Embedding by Adversarial PAC-Bayesian Learning Daizong Ding,1 Mi Zhang ,1 Xudong Pan,1 Min Yang,1 Xiangnan He2 1School of Computer Science, Fudan University 2School of Information Science and Technology, University … It turns out that representations learned by robust models address the aforementioned shortcomings and make significant progress towards learning a high-level encoding of inputs. • ImageNet accuracy likely correlates with the quality of features that a model learns, but it may not fully capture the downstream utility of those features. This is reflected in the table below, in which we compare the accuracies of the best standard model and the best robust model (searching over the same set of hyperparameters and architectures): The following graph shows, for each architecture and downstream classification task, the performance of the best standard model compared to that of the best robust model. In a recent collaboration with MIT, we explore adversarial robustness as a prior for improving transfer learning in computer vision. We identify the pervasive brittleness of deep networks' learned representations as a fundamental barrier to attaining this goal. More broadly, our results indicate adversarial robustness as a promising avenue for improving learned representations. Adversarial training can therefore be used … While existing work in robust deep learning has focused on small pixel-level ℓp norm-based perturbations, this may not account for perturbations encountered in several real world settings. Evaluation of adversarial robustness is often error-prone leading to overestimation of the true robustness of models. Figure 1: Dangers of using non-robust representation learning. Welcome to my page! We find that adversarially robust models outperform their standard counterparts on a variety of downstream computer vision tasks. In many such cases although test data might not be available, broad specifications about the types of perturbations (such as an unknown degree of rotation) may be known. Post by Sicheng Zhu. It turns out that representations learned by robust models address the aforementioned shortcomings and make significant progress towards learning a high-level encoding of inputs. Based on the robustness python library. In this work, we show that robust optimization can be re-cast as a tool for enforcing priors on the features learned by deep neural networks. These works have found that although these adversarially robust models tend to … We can either use standard models that have high accuracy but little robustness on the source task; or we can use adversarially robust models, which are worse in terms of ImageNet accuracy but are robust and have the “nice” representational properties (see Figure 3). 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