Therefore, by definition, it has such weaknesses. point out that … deep neural networks learn input-output mappings that are fairly discontinuous to a significant extent. QWR is an extension of Advantage Weighted Regression (AWR), an off-policy actor-critic algorithm that performs very well on continuous control tasks, also in the offline setting, but has low sample efficiency and struggles with high-dimensional observation spaces. We explain why this notion is interesting to study and use it to prove the following. The columns of Table 2 sho. Intriguing properties of neural networks. supervised object segmentation using classification ConvNets. object detection and semantic segmentation. varied number of layers, activations or trained on dif, we use one neural net to generate a set of adversarial examples, we find that these examples are still, statistically hard for another neural network even when it was trained with dif, These results suggest that the deep neural networks that are learned by backpropagation have nonin-, tuitive characteristics and intrinsic blind spots, whose structure is connected to the data distribution, For the MNIST experiments, we use regularization with a weight decay of, experiments we split the MNIST training dataset into two disjoint datasets, Traditional computer vision systems rely on feature extraction: often a single feature is easily inter-, individual coordinates of the feature space, and link them back to meaningful variations in the input, were applied to computer vision problems. We show that the answer is yes, and that the resulting system is simple, scalable, and boosts mean average precision, relative to the venerable deformable part model, by more than 40% (achieving a final mAP of 48% on VOC 2007). Adversarial examples for a randomly chosen subset of MNIST compared with randomly distorted examples. The quality of these But, we do not know or have control of what is happening inside the model. We exploit the linear The examples are strictly randomly chosen. This article provides an overview of this progress and represents the shared views of four research groups that have had recent successes in using DNNs for acoustic modeling in speech recognition. The goal of this paper is to find good qualita-tive interpretations of high level features represented by such models. We investigate the adversarial robustness of CNNs from the perspective of channel-wise activations. Each of our models were trained with L-BFGS until, of our models are simple linear (softmax) classifier without hidden units (FC10(, adversarial examples in this extreme setting as well.T, network with two hidden layers and a classifier. Thus it is very difficult to interpret the model and it … In particular, we show how to realize this vision in a simplified setting. examples—works as expected. The computation is dominated by the and 4 compare the natural basis to the random basis on the trained network. This suggests a simple regularization of the parameters, consisting in penalizing. their class, including objects for which no bounding box labels have been Specifically, we find that we And that in particular, for a small enough radius, problems. Join ResearchGate to discover and stay up-to-date with the latest research from leading experts in, Access scientific knowledge from anywhere. We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 dif- ferent classes. While their expressiveness is the reason they succeed, it also causes them to learn uninterpretable solutions … deliver impressive accuracy but each image evaluation requires millions of The randomly chosen examples on the left are recognized correctly as cars, while. However, as the training progresses, the training data becomes less and less attackable, undermining the robustness enhancement. We then show that this separation has interesting implications for adversarial robustness. First, we find that there is no distinction between individual high level stddev=0.058), Distorted for FC100-100-10 amplified to stddev=, Distorted for FC123-456-10 amplified to stddev=, Distorted for FC100-100-10’ amplified to stddev=, Table 4: Cross-training-set generalization error rate for the set of adversarial examples generated for different. In Advances in Neural Information Processing Systems 25, pages 1106–1114, 2012. take advantage of state-of-the-art fully convolutional network structure for In this paper we report two such properties. These results further justify the use of "deep " vs. "shallower" repre- sentations, but suggest that mechanisms beyond merely stacking one autoencoder on top of another may be important for achieving invariance. Such global analyses are useful in that they can make us. analyzed the semantic meaning of various units by finding the set of inputs that maximally, A simple fully connected network with one or more hidden layers and a Softmax, A classifier trained on top of an autoencoder. ∙ 0 ∙ share . uninterpretable solutions that could have counter-intuitive properties. Odd columns correspond to original images, and even columns correspond to distorted counterparts. Artificial neural network(ANN)s have been widely used in image processing, speech recognition, game, and medical diagnosis. It suggests that it is the space, rather than the individual units, that contains of the semantic information in the high layers of neural networks. image encoder that extracts high level info from image, a segmentation encoder Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. state of the art performance on speech and visual recognition tasks. We refer to this netw, is a held-out set of images from the data distribution that the network was not trained on, is the natural basis vector associated with the, . To enhance adversarial robustness, adversarial training learns deep neural networks on the adversarial variants generated by their natural data. 06/10/2019 ∙ by Cihang Xie, et al. The first one generates an image, which maximises the class In particular, we propose : Tests of the generalization of adversarial instances on MNIST. Experiments demonstrate that the GIF can indeed enhance adversarial robustness on various adversarial training methods and various datasets. While their expressiveness is the reason they succeed, it also causes them to learn uninter- pretable solutions that could have counter-intuitive properties. More formally, we find that images x′ are semantically related to each other, for many x′ such that x′ = arg max x ∈ I φ(x), v This suggests that the natural … given image and class. Such slightly manipulated images aimed at deceiving the classifier are known as adversarial images. We can cause the network to misclas-, sify an image by applying a certain hardly perceptible perturbation, which is found, by maximizing the network’s prediction error, these perturbations is not a random artifact of learning: the same perturbation can, cause a different network, that was trained on a different subset of the dataset, to, Deep neural networks are powerful learning models that achiev. in vision, language, an d other AI-level tasks), one needs deep architec- tures. to be significantly more useful than those on the input or lower layers. vectors provide state-of-the-art performance on our test set for measuring (Left) is a correctly predicted sample, (center) difference between correct image, and image predicted incorrectly magnified by 10x (values shifted by 128 and clamped), (right) adversarial example. the input image. Although the autoencoder, based version seems most resilient to adversarial examples, it is not fully immune either, Still, this experiment leaves open the question of dependence over the training set. score [Erhan et al., 2009], thus visualising the notion of the class, captured While deformable part models have become quite popular, their value had not been demonstrated on difficult benchmarks such as the PASCAL challenge. In this paper we report two such properties. Deep neural networks excel at finding hierarchical representations that solve complex tasks over large datasets. The paper introduces two key properties of deep neural networks: Semantic meaning of individual units. networks disentangle variation factors across coordinates. The error induced by a random distortion to the same examples is displayed in the last row. structure present within the convolutional filters to derive approximations of the last feature layer form a distinguished basis which is particularly useful for extracting seman-, indistinguishable from the coordinates of. A deeper understanding of the problem could lead to a better comprehension of how information is processed and encoded in neural networks and, more in general, could help to solve the issue of interpretability in machine learning. Both methods are independent of the chosen attack and leverage random projections of the original inputs, with the purpose of exploiting both dimensionality reduction and some characteristic geometrical properties of adversarial perturbations. it takes less than a day to learn high quality word We hope that such techniques will allow researchers in deep architec-tures to understand more of how and why deep architectures work. The existence of, by these adversarial negatives, which are indistinguishable from the regular e, explanation is that the set of adversarial negativ, (or rarely) observed in the test set, yet it is dense (much like the rational numbers), and so it is found. two, We present techniques for speeding up the test-time evaluation of large We The adversarial examples represent low-probability (high-dimensional) “pockets” in the manifold, which are hard to efficiently find by simply randomly sampling the input around a given example. individual units, that contains of the semantic information in the high layers Intriguing properties of neural networks Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, Rob Fergus Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. For web page which are no longer available, try to retrieve content from the of the … that extracts high level info from segmentation, and a hybrid decoder that The same framework is also competitive with state-of-the-art semantic segmentation methods, demonstrating its flexibility. 12/21/2013 ∙ by Christian Szegedy, et al. Our framework combines powerful computer vision techniques for generating bottom-up region proposals with recent advances in learning high-capacity convolutional neural networks. to a correctly classified input image, so that it is no longer classified correctly. We evaluate the robustness of CNNs against four types of white-box attacks: FGSM. generic object detection. Extensive experiments on benchmark datasets including CIFAR10 and SVHN clearly verify the hypothesis and CIFS's effectiveness of robustifying CNNs. In practice, label-noise issues widely exist in real-world training datasets, and learning with noisy labels seems inevitable. In addition, the specific nature of these perturbations is the following box-constrained optimization problem: This penalty function method would yield the exact solution for, losses, however neural networks are non-con, port by informal evidence and quantitative e, 1. system, our key design is a three-layer generative structure consisting of We prove an exponential separation for the sample complexity between the standard PAC-learning model and a version of the Equivalence-Query-learning model. We find that convolutional deep belief networks learn substantial ly more invariant features in each layer. techniques, based on computing the gradient of the class score with respect to label and the statistical structure of the original inputs. This paper d iscusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks. put, because small perturbation cannot change the object category of an image. We perform an analysis of AWR that explains its shortcomings and use these insights to motivate QWR. manipulations of the inputs that are maliciously crafted to fool networks into incorrect predictions. ... At its core, DML relies on state-of-the-art deep learning techniques for training models that output lower-dimensional semantic feature embeddings from high-dimensional inputs. paper we report two such properties. floating point operations, making their deployment on smartphones and establish the connection between the gradient-based ConvNet visualisation Second, we find that deep neural networks learn input-output mappings that are, fairly discontinuous to a significant extent. corresponding column of the upper part of T, models and the error rates are displayed in the lower part of T, that the adversarial examples remain hard for models trained even on a disjoint training set, although, their effectiveness decreases considerably, The previous section showed examples of deep networks resulting from purely supervised training. that significantly reduce the required computation. While their expressiveness is the reason they succeed, it also causes them to learn uninterpretable solutions that could have counter-intuitive properties. In their paper Intriguing properties of neural networks they introduced the term of adversarial examples, which are maliciously designed input images to purposely fool the model into predicting a wrong class. model for weakly-supervised localization). Convolutional neural networks (CNNs) are fragile to small perturbations in the input images. adversarial negatives appears, and thus this issue should be addressed in a future research. We show through representations is measured in a word similarity task, and the results are Adversarial examples generated for AlexNet [9]. Our experiments show that any random direction, This suggests that the natural basis is not better than a random basis for inspecting the properties, First, we evaluated the above claim using a con, basis, and Figure 2 shows images that maximize the activation in random directions. Results. Our sys- tem achieves a two-fold improvement in average precision over the best performance in the 2006 PASCAL person de- tection challenge. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. another based on the spectrum of the graph Laplacian. We show that, perhaps counter-intuitively, such interpretation is possible at the unit level, that it is simple to accomplish and that the results are consistent across various techniques. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. In other words, it is assumed that is possible for the output unit to assign non-, significant (and, presumably, non-epsilon) probabilities to re. suggest that back-feeding adversarial examples to training might improve generalization of the re-, of which is continuously replaced by newly generated adversarial examples and which is mixed into, ference between correct image, and image predicted incorrectly magnified by 10x (values shifted by 128 and, clamped), (right) adversarial example. How can we humans understand these learned representations? Images within each row share many semantic properties. Although the underlying neural networks produce good accuracy on naturally occurring samples, they are vulnerable to adversarially-perturbed samples that reduce performance. All images in the right column are predicted to be an "ostrich, Struthio camelus". (maximum stimulation in a random basis). The main property of deep learning is that it is able to identify and extract the features automatically through back propagation. Can a large convolutional neural network trained for whole-image classification on ImageNet be coaxed into detecting objects in PASCAL? Our analysis provides a detailed investigation of this new approach that can serve as a basis for alternative adversarial example detection methods that do not need to modify the original CNN classifier neither work on the raw high-dimensional pixels as features to defend against attacks. Corpus ID: 28912221. end-to-end learning in normal and extensive form games Second, we find that deep neural networks learn input-output mappings that Using experiments on three commonly-used DML datasets, we demonstrate 5-76 fold increases in adversarial accuracy, and outperform an existing DML model that sought out to be robust. Average distortion based on 64 examples is 0.006508. For many pattern recognition tasks, the ideal input feature would be invariant to multiple confounding properties (such as illumination and viewing angle, in com- puter vision applications). We shall, regarding the complexity of the representations learned by a deep neural network [, led to a correct classification of a given visual input instance (in other words, one can use a trained. In both cases. compared to the previously best performing techniques based on different types Input and output mapping are discontinuous As shall be described, the optimization problem proposed in this work.