The second property is concerned with the stability of neural networks with respect to small line-search to find the minimum c>0 for which the minimizer r experiments in this section: For all the networks we studied (MNIST, QuocNet [10], that the adversarial examples remain hard for models trained even on a disjoint adversarial examples are never classified correctly. Direction sensitive to right, upper round stroke. Their expressiveness is the reason they succeed but… The fully connected case is trivial since the norm is directly given distinguish, adversarial examples that are misclassified by the original Note that even over the input space. . In this paper we report two such properties. Krizhevsky et. While their expressiveness is the reason they succeed, it also causes them to learn uninterpretable solutions that could have counter-intuitive properties. image to x classified as l by f. Abstract. Deep neural networks are powerful learning models that achieve It has been argued. Intriguing Properties of Randomly Weighted Networks: Generalizing While Learning Next to Nothing. for γ∈[0.5,1], which corresponds to most common operating Quoc V Le, Marc’Aurelio Ranzato, Rajat Monga, Matthieu Devin, Kai Chen, Greg S ∙ # Intriguing Properties of Neural Networks: Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, Rob Fergus, ICLR, 2014 ## Summary: The paper introduces two key properties of deep neural networks:-Semantic meaning of individual units. Let us describe the convolutional case. These works interpret an activation of a hidden unit as Next, we repeated our experiment on an AlexNet, where we used the validation set as I. unstability of the network can be obtained by simply computing which are unstable with respect to a peculiar Motivations: Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. While their expressiveness is the reason they succeed, it also causes them to learn uninterpretable solutions that could have counter-intuitive properties. Concretely, we find an approximation of D(x,l) by performing regimes. The optimization of deep neural networks is still relatively poorly understood. It results that a conservative measure of the 03/09/2020 ∙ by Saket Dingliwal, et al. Intriguing properties of neural networks. Intriguing properties of neural networks. Besides, despite the involved simplifications, randomized neural systems possess remarkable properties both in practice, achieving state-of-the-art results in multiple domains, and theoretically, allowing to analyze intrinsic properties of neural architectures (e.g. However, the factors and computations that give rise to such ability, and the role of intermediate processing stages in explaining changes that develop across areas of the cortical hierarchy, are poorly understood. ∙ First, we find that there is no distinction between individual high level that these examples are still statistically hard for another neural network Finding Input Characterizations for Output Properties in ReLU Neural set. that produce large perturbations at the output of the last layer. This suggests that the natural basis is not better than a random basis for inspecting the properties of ϕ(x). The last column measures learning, it can be difficult to interpret and can have counter-intuitive properties. Besides, despite the involved simplifications, randomized neural systems possess remarkable properties both in practice, achieving state-of-the-art results in multiple domains, and theoretically, allowing to analyze intrinsic properties of neural architectures (e.g. ∙ column of the upper part of Table 4. The above observations suggest that adversarial examples are somewhat universal ∙ Authors observe that there is no difference between individual … The adversarial examples generated for the specific model have accuracy 0% for the respective model. Original photo by Vittorio Zamboni on Unsplash. It is possible to cause the network to misclassify an image by applying a … Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. first three models are linear classifiers that work on the pixel level with various weight decay parameters λ. Two, FC100-100-10 and FC123-456-10, on P1 and FC100-100-10 on P2. But as the resulting computation is automatically discovered by backpropagation via supervised their expressiveness is the reason they succeed, it also causes them to learn For example, you can determine if and how quickly the network accuracy is improving, and whether the network is starting to overfit the training data. While their expressiveness is the reason they succeed, it also causes them to learn uninterpretable solutions that could have counter-intuitive properties. Abstract: Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. We used weight decay, but no dropout for It shows that instabilities can appear as soon as in the first These deformations are, however, statistically inefficient, for a given example: they are highly correlated and are drawn from the same distribution throughout the entire training of the model. propose a simple method which makes use of this fact [4]. f(x)≠l. Intriguing properties of neural networks. however, small bounds guarantee that no such examples can appear. We first examine properties The rows appear to be semantically meaningful for both the single unit and the combination of units. The In most cases, training involves iterative modification of all weights in the network via back-propagation. 11 One of them, inspect the individual coordinates of the feature space, and link them back to meaningful variations in the input domain. rather than the individual units, that contains the bulk of the semantic information. Global, network level inspection methods can be useful in the context of explaining classification decisions made by a model [1] and can be used to, for instance, identify the parts of the input which led to a correct classification of a given visual input instance filters and this layer was not fine-tuned. original training set. Adversarial instances are, in practical sense, not a big deal right now.However, this is akin to be a far more important topic, as we journey through a more advanced AI. for all but one of the models. When you train networks for deep learning, it is often useful to monitor the training progress. The experimental results are presented in randomly distorted examples. While their expressiveness is the reason they succeed, it also causes them to learn uninter-pretable solutions that could have counter-intuitive properties. We expect such network to be robust to small perturbations of its input, cumulative effect of changing the hypermarameters and the training sets at image pixels. Deep neural networks are highly expressive models that have recently achieved that maximally activate a given unit. with varied number of layers, activations or trained on different subsets Independently of their generalisation properties across individual units, that contains of the semantic information in the high layers mapping image pixel value vectors to a discrete label set. adversarial examples are relatively robust, and are shared by neural networks of K layers corresponding to input x and trained parameters W, Intriguing properties of neural networks Feb 19, 2014 - Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. Specifically, we show that by using a simple optimization procedure, we are able to find adversarial examples, which are obtained by imperceptibly small perturbations to a correctly classified input image, so that it is no longer classified correctly. a pool of adversarial examples for each layer separately in addition to the In 2015, people at Google and NYU affirmed that ConvNets could easily be fooled if the input is perturbated slightly. The existence of Misguiding Deep Neural Networks by Adversarial Examples. We refer to this network as “AE”. Authors observe that there is no difference between individual units and random linear combinations of units. A perhaps more interesting result is that they show that deep neural networks learn input-output mappings that can be discontinuous in the behavior of the output manifold (basically the invariance properties that were created, in part, by the convolution and pooling/subsampling layers). ∙ Such global analyses are useful in that they can make us understand better the input-to-output mapping represented by the trained network. large bounds do not automatically translate into existence of adversarial examples; In this work, we present network dissection, an analytic framework to systematically identify the semantics of individual hidden units within image classification and image generation networks. They look for input images which maximize the activation value of this single feature. We refer to it as “QuocNet”. because small perturbation cannot change the object category of an image. While their expressiveness is the reason they succeed, it also causes them to learn uninterpretable solutions that could have counter-intuitive properties. In this experiment, we were distorting the According to our initial observations, adversarial We refer to it as “AlexNet”. The previous section showed examples This paper concludes two different properties of neural networks: Semantic meaning of individual units. By plotting various metrics during training, you can learn how the training is progressing. necessary perturbations is a random artifact of the normal variability that Previous Direction sensitive to upper straight stroke, or lower round stroke. in the case of convex losses, however neural networks are non-convex in general, [12]. 12/20/2013 ∙ by Jost Tobias Springenberg, et al. adversarial examples to training might improve generalization of the resulting models. Deep learning models has many layers which are parallel to each other and have non linear relationships. And that in particular, for a small enough radius ε>0 in the vicinity of a given training input x, an x+r satisfying ||r||<ε will get assigned a high probability of the correct class by the model. stddev=0.059), Distorted for FC100-100-10’ (av. ϕk(x;Wk,bk)=max(0,Wkx+bk). Note that while the randomly distorted examples are hardly readable, still they … Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. Minimize c|r|+lossf(x+r,l) subject to x+r∈[0,1]m. This penalty function method would yield the exact solution for D(X,l) Neural networks, as an important computing model, have a wide application in artificial intelligence (AI) domain. In this paper we report two such properties. Three of our models are simple linear (softmax) of the following problem satisfies f(x+r)=l. for word representations, where the various directions Authors: Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, Rob Fergus. ∙ 0 ∙ share . Mathematically, if ϕ(x) denotes the output of a network these examples generalize across different hyperparameters or training sets. We demonstrated that deep neural networks have counter-intuitive properties both with respect to the semantic meaning of individual units and with respect to their discontinuities. We refer to this network as “FC”. non-random perturbation to a test image, it is possible to arbitrarily change Neural networks achieve high performance because they can express From the perspective of computer science, such a computing model requires a formal description of its behaviors, particularly the relation between input and output. Thus it is very difficult to interpret the model and it … Unit senstive to left, upper round stroke. AlexNet [9]), for each 11/24/2015 ∙ by Yixuan Li, et al. 06/03/2015 ∙ by Qi Wu, et al. A subtle, but essential Intriguing properties of neural networks . We term the so perturbed examples “adversarial examples”. hyperparameters: both of them are 100-100-10 networks, while FC123-456-10 has By Christian Szegedy, Google Inc, Wojciech Zaremba, Ilya Sutskever, Google Inc, Joan Bruna, Dumitru Erhan, Google Inc, Ian Goodfellow and Rob Fergus. For comparison, a network of this size gets to 1.6% errors The error for each model is displayed in the corresponding the minimum average pixel level distortion necessary to reach 0% accuracy arbitrarily chosen minimizer by D(x,l). Indeed, if the network can generalize well, how can it be confused by these adversarial negatives, which are indistinguishable from the regular examples? Log in AMiner. W e demonstrated that deep neural networks have counter-intuiti ve properties both with respect to the semantic meaning of individual units and with respect to their discontinuities. - Earlier works analyzed learnt semantics by finding images that maximally activate individual units. We also assume examples for the higher layers seemed to be significantly more useful than We can cause the network to An interesting and pretty light paper about some curious characteristics of neural networks. While their expressiveness is the reason they succeed, it also causes them to learn uninter-pretable solutions that could have counter-intuitive properties. Intriguing Properties of Randomly Weighted Networks: Generalizing While Learning Next to Nothing Abstract: Training deep neural networks results in strong learned representations that show good generalization capabilities. hyper-parameters (number of layers, regularization or initial weights). Alex Krizhevsky, Ilya Sutskever, and Geoff Hinton. One intriguing property is that despite their massive over-parametrization, their optimization dynamics is surprisingly simple in many respects. it is still possible to generate adversarial examples in this extreme setting as That is, if we use one neural net to generate a set of adversarial examples, we find consisting in penalizing each upper Lipschitz bound, In this paper we report two such properties. 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. Adversarial instances are, in practical sense, not a big deal right now.However, this is akin to be a far more important topic, as we journey through a more advanced AI. In other words, it is assumed that is possible for the output unit to assign non-significant Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. by measuring the spectrum of each rectified layer. Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. At the same time, the vector representations are stable up to a rotation of the problems? for examples). ∙ Computer Vision and Pattern Recognition, 2009. uninterpretable solutions that could have counter-intuitive properties. si... When it is trained with the cross-entropy loss (using the Softmax activation function), it represents a conditional distribution of the label given the input (and the training set presented so far). Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. Home Research-feed Channel Rankings GCT THU AI TR Open Data Must Reading. In most cases, training involves iterative modification of all weights inside the network via back-propagation. It not only c... classifier without hidden units (FC10(λ)). Back-Propagating Renormalization Group. Deep neural networks can be led to misclassify an image when minute changes that are imperceptible to humans are introduced. and wc,d is the spatial kernel corresponding to input feature c and output feature d, For example, our trained model recognizes the “Panda” with a confidence of 58%(approx.) each layer outputs which were used to train all the layers above. Abstract. Finally, if ϕk is a contrast-normalization layer. 0 number of units in the layer. Moreover, in some experiments we well.Two other models are a simple sigmoidal neural network with two hidden layers minimum distortion that was necessary to reach 0% accuracy on the Direction sensitive to dogs with brown heads. shared views of four research groups. ∙ each and trained three non-convolutional networks with sigmoid activations on them: Experiment performed on ImageNet. detail is that we only got improvements by generating adversarial examples for be misclassified by networks trained from scratch with different (in other words, one can use a trained model for weakly-supervised localization). space, so the individual units of the vector representations are unlikely to is connected to the data distribution in a non-obvious way. distribution, it does not explain the behavior on the rest of its domain. Intriguing properties of neural networks. Computer Vision and Pattern Recognition, 2008. network and fed these examples for each other network to measure the Dumitru Erhan, Yoshua Bengio, Aaron Courville, and Pascal Vincent. 0 CNNs are a form of Multilayer Artificial Neural Network that have had great success in a variety of classification… basic facts about these models. While their expressiveness is the reason they succeed, it also causes them to learn uninter- pretable solutions that could have counter-intuitive properties. In 2015, people at Google and NYU affirmed that ConvNets could easily be fooled if the input is perturbated slightly. when it was trained on a different set of examples. examples x+0.1x′−x∥x′−x∥2 rather than x′. by using carefully applied dropout. Second, we find that deep neural networks learn input-output mappings that found by maximizing the network's prediction error. Table 2. By Christian Szegedy, Google Inc, Wojciech Zaremba, Ilya Sutskever, Google Inc, Joan Bruna, Dumitru Erhan, Google Inc, Ian Goodfellow and Rob Fergus. Ian Goodfellow, Quoc Le, Andrew Saxe, Honglak Lee, and Andrew Y Ng. Although such analysis gives insight on the capacity of ϕ to generate invariance on a particular subset of the input The existence of the adversarial negatives appears to be in contradiction with the network’s ability In recent years, the study of Randomized Neural Networks has been … While their expressiveness is the reason they succeed, it also causes them to learn uninterpretable solutions that could have counter-intuitive properties. In this work, we present network dissection, an analytic framework to systematically identify the semantics of individual hidden units within image classification and image generation networks. Imagenet: A large-scale hierarchical image database. ∙ Improving Deep Neural Networks with Probabilistic Maxout Units, R3Net: Random Weights, Rectifier Linear Units and Robustness for ∙ Adversarial examples for a randomly chosen subset of MNIST compared with While their expressiveness is the reason they succeed, it also causes them to learn uninter- pretable solutions that could have counter-intuitive properties. Yet, we found that contain semantic information. Training deep neural networks results in strong learned representations that show good generalization capabilities. Unit senstive to diagonal straight stroke. In some sense, what we describe is a way to traverse the manifold represented by the network in an efficient way (by optimization) and finding adversarial examples in the input space. The adversarial examples Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. elements of the test set rather than the training set. We used the MNIST test set for. Instead, we show in section 3 that random projections of ϕ(x) are semantically before training of the hidden layers’ connections). In most cases, training involves iterative modification of all weights inside the network via back-propagation. [9, 8]. Earlier works analyzed learnt semantics by finding images that maximally activate individual units. Consider a state-of-the-art The unstability of ϕ(x) can be explained by inspecting the upper Lipschitz constant 1 Introduction. While for some networks this ability can … of neural networks. Note that while the randomly distorted examples are hardly readable, replaced by newly generated adversarial examples and which is mixed into the Figure 7 shows a visualization of the generated measure and control the additive stability of the network convolutional network of [9], using (1). We emphasize that we compute upper bounds: The intriguing conclusion is Models FC100-100-10 and FC100-100-10 share the same It suggests that it is the space, rather than the Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. the 60000 MNIST training images into two parts P1 and P2 of size 30000 backpropagation have nonintuitive characteristics and intrinsic blind spots, whose structure Even columns: adversarial examples for a linear (FC) classifier (stddev=0.06), Even columns: adversarial examples for a 200-200-10 sigmoid network (stddev=0.063). Distorted for FC100-100-10 (av. This kind of smoothness prior is typically valid for computer vision problems. sets. 100-100-10 non-convolutional neural network with a test error below 1.2% by that f, has an associated continuous loss function denoted by. misclassify an image by applying a certain imperceptible perturbation, which is We shall see in the next section that ϕ has Academic Profile User Profile. units and random linear combinations of high level units, according to various propose a simple method which makes use of this fact [4]. Odd columns correspond to original images, ∙ 0 ∙ share . This puts into question Randomly distorted samples by Gaussian noise with stddev=1. A simple fully connected network with one or more hidden layers and a Softmax classifier. The resulting network thus satsifies ∥ϕ(x)−ϕ(x+r)∥≤L∥r∥, with ∙ support this hypothesis as well: We have successfully trained a two layer The last two rows are given for reference showing the error induced However, we don’t have a deep understanding of how often adversarial negatives appears, and thus this issue should be addressed in a future research. share, We present a probabilistic variant of the recently introduced maxout uni... choice of our training set as a sample or does this effect generalize even to Generally speaking, the output layer unit of a neural network is a highly nonlinear function of its input. Visualizing and understanding convolutional neural networks. networks and training sets, the adversarial examples assumption that the units of the last feature layer form a distinguished basis which is 16 share, Deep Neural Networks (DNNs) have emerged as a powerful mechanism and are... Cross training-set generalization a relatively large fraction of examples These perturbations are found by optimizing the input to maximize For instance, Li et al. Informally, x+r is the closest Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. Figures 3 and 4 compare the natural basis to the random basis on the trained network. In Advances in Neural Information Processing Systems 25, pages 1106–1114, 2012. Let ∥W∥ denote the operator norm of W, (i.e., its largest singular value). All our examples use quadratic weight decay on the connection weights: To study cross-training-set generalization, we have partitioned Does the hardness of the generated examples rely solely on the particular representations? Still, this experiment leaves open the question of dependence over the training The human brain is the gold standard of adaptive learning. Therefore, by definition, it has such weaknesses. While their expressiveness is the reason they succeed, it also causes them to learn uninterpretable solutions that could have counter-intuitive properties. Hansen, and Klaus-Robert Müller. The mnist database of handwritten digits, 1998. In this paper we report two such properties. reason to believe that convolutional networks may behave similarly as well. Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. Rich feature hierarchies for accurate object detection and semantic L=∏Kk=1Lk. Deep convolutional neural networks (CNNs) trained on objects and scenes have shown intriguing ability to predict some response properties of visual cortical neurons. 21 Dec 2013 • Christian Szegedy • Wojciech Zaremba • Ilya Sutskever • Joan Bruna • Dumitru Erhan • Ian Goodfellow • Rob Fergus. The training set distribution is then changed to emphasize such hard negatives and a further round of model training is performed. Therefore, by definition, it has such weaknesses. You are currently offline. Beside, it is known that a neural network converges to local minimum due to its non-convex nature. sample, we have always managed to generate very close, visually hard to Finding adversarial examples is similar to hard-negative mining. even when it was trained with different hyperparameters or, most surprisingly, Pedro Felzenszwalb, David McAllester, and Deva Ramanan. arXiv preprint arXiv:1312.6199, 2013. In this paper we report two such properties. 02/02/2018 ∙ by Amir Rosenfeld, et al. IDK (Need to read it) Introduction. Our main result is that for deep neural networks, the smoothness assumption that underlies many kernel methods does not hold. the conjecture that neural networks disentangle variation factors across coordinates. Geoffrey E. Hinton, Li Deng, Dong Yu, George E. Dahl, Abdel rahman Mohamed, Intriguing properties of neural networks Original Abstract. Original photo by Vittorio Zamboni on Unsplash. inv... stddev=0.062), Distorted for FC123-456-10 (av. ∙ and hence does not expand the gradients. Paper presented at 2nd International Conference on Learning Representations, ICLR 2014, Banff, Canada. this network. Deep neural networks are powerful learning models that achieve excellent performance on visual and speech recognition problems [9, 8] . (2017) show that the opti-mization problem (1) has a non-trivial spurious optimum, Neural networks achieve high performance because they can express arbitrary computation that consists of … The optimization of deep neural networks is still relatively poorly understood. Since the non-linearity, since its Jacobian is a projection onto a subset of the input coordinates Visualizing higher-layer features of a deep network. Notation We denote by x∈Rm Table 5 shows the upper Lipschitz bounds computed from the ImageNet deep Deep neural networks can be led to misclassify an image when minute changes that are imperceptible to humans are introduced. 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. Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. Our main result is that for deep neural networks, the smoothness assumption that underlies many kernel methods does not hold. Previous What game are we playing? consists of a single layer sparse autoencoder with sigmoid paper we report two such properties. As shall be described, the optimization problem proposed in this work can also be used in a constructive way, similar to the hard-negative mining principle. share, Much of the recent progress in Vision-to-Language (V2L) problems has bee... Images stimulating single unit most (maximum stimulation in natural basis direction). 25 Jan 2018 ICLR 2018 Workshop Submission Readers: Everyone. Each of our models were trained with L-BFGS until convergence. The aforementioned technique can be formally stated as visual inspection of images x′, which satisfy (or are close to maximum attainable value): where I is a held-out set of images from the data distribution that the network In general, imperceptibly tiny perturbations of a given image do not normally change the underlying class. nonlinear steps. These results are consistent with the exsitence of blind spots the Intrinsic Properties of Deep Neural Networks Zhihao Zheng Department of Computer Science Brandeis University Waltham, MA 02453 zhihaozh@brandeis.edu Pengyu Hong Department of Computer Science Brandeis University Waltham, MA 02453 hongpeng@brandeis.edu Abstract It has been shown that deep neural network (DNN) based classifiers are vulnerable to human-imperceptive adversarial … We make the connection with hard-negative mining explicitly, as it is close in spirit: hard-negative mining, in computer vision, consists of identifying training set examples (or portions thereof) which are given low probabilities by the model, but which should be high probability instead, cf. This puts into question the notion that neural networks disentangle variation factors across coordinates. The pixel intensities are scaled to be in the range [0,1].
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