visualizing and understanding recurrent networks iclr 2016

We decompose the featuremap r into two parts (rU,P,r¯¯¯¯¯¯U,P), where r¯¯¯¯¯¯U,P are unforced components of r: An object is caused by U if the object appears in xi and disappears from xa. Bolei Zhou, Yiyou Sun, David Bau, and Antonio Torralba. 2015. Just replace the word(s) in the caption with strong attention(s), in subject to little change in the attention(s), the \(\alpha\)(s), and then we can get the “customized” linguistic regularity perseving image. BIO: Chris Dyer is an assistant professor in the Language Technologies Institute and Machine Learning Department at Carnegie Mellon University. Figure 3 illustrates two such units. 2015. arXiv pre-print. Found inside – Page 100Visualizing and understanding recurrent networks. ... In ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA), pages 380–392, June 2016. DOI: 10.1109/isca.2016.41. 85, 86 [83] Jung Kuk Kim, Phil Knag, ... Improving performance of recurrent neural network with relu nonlinearity pdf. (a) unit118 in layer4 (a) the lowest-FID unit that is manually flagged as showing artifacts (b) the highest-FID unit that is not manually flagged (c) the highest-FID unit overall, which is also manually flagged. A key method in visualization methods for deep learning is the display of the network architectures. Deep Recurrent Neural Networks (RNN) is increasingly used in decision-making with temporal sequences. Object detectors emerge in deep scene cnns. This is a full transcript of the lecture video & matching slides. ∙ Stanford University ∙ 0 ∙ share RNNs), and specifically a variant with Long Short-Term Memory (LSTM), are enjoying renewed interest as a. Proc. The threshold tu,c is chosen to be informative as possible by maximizing the information quality ratio I/H (using a separate validation set), that is, it maximizes the portion of the joint entropy H which is mutual information I (Wijaya et al., 2017). Found inside – Page 260Simonyan, K., Zisserman, A.: Very deep convolutional networks for large scale. In: Conference ICLR (2015) 2. ... Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional neural networks (2013) Goodfellow, I., Bengio, Y., ... Nguyen et al., Deep neural networks are easily fooled: High confidence predictions for unrecognizable images, CVPR 2015. Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, et al. Both of the work is amazing and thought-provoking. [26] Matthew D. Zeiler and Rob Fergus. Ian Goodfellow, Google Deep neural networks can be trained to transfer concepts from one domain to another (more interpretable) domain. Found inside – Page 438Ronneberger, O., Fischer, P., Brox, T.: 'U-Net: Convolutional networks for biomedical image segmentation. In: MICCAI (2015). ... In: ICLR (2015). ... Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional neural networks. In Proceedings of ICLR Workshop. The forest convolutional network: compositional distributional semantics with a neural chart and without binarization. Since we use a zero-order optimization method, our framework is model-agnostic, in the sense that the machine learning model that we aim to explain is a black-box. 08/15/2016 ∙ by Zhiqiang Xia, et al. First, they extend the DRAW model to be conditional when generating, so that it adapts to a conditional generative model, namely alignDRAW. Thresholding unit #37 layer 4 of a living room generator matches ‘sofa’ segmentations with IoU=0.29. By incorporating context information in classifier, the output at every time step t depends not only on the hidden representation , but also on the input history. Visualizing and Understanding Recurrent Networks. On the importance of single directions for generalization. Alternatively, when a GAN sometimes produces terribly unrealistic images (Figure 1f), what causes the mistakes? (r↑u,P>tu,c) produces a binary mask by thresholding the r↑u,P at a fixed level tu,c. Five different sets of units have been ablated related to a specific object class. A FID threshold selecting the top 20 FID units will identify 10 (of 20) of the manually flagged units. In the second part of the talk I’ll show a direct loss minimization approach to train deep neural networks, which provably minimizes the task loss. Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Although this intervention is the same in each case, the effects vary widely depending on the objects’ surrounding context. Although every such unit-maximizing subset of images represents a skewed distribution, we find that the per-unit FID scores fall in a wide range, with most units scoring well in FID while a few units stand out with bad FID scores: many of them were also manually flagged by humans, as they tend to activate on images with clear visible artifacts. Conference: ICLR 2017 Comparing layers of a progressive GAN trained to generate LSUN living room images. future have more reason to be concerned about the virus. P. Sloan Fellowship, Microsoft This includes (even not formally published) research papers sorted by year and topics as well as links to tutorials and code and other related Tractable Probabilistic Models (TPMs). One point of criticism is that language users create and understand new words all the time, challenging the finite vocabulary assumption. ICLR 2016: List of accepted papers: (Update 2: If you are looking for the ICLR 2017 papers, they are in open review here ) (update: All . Found inside – Page 265A. Krizhevsky, I. Sutskever and G. E. Hinton, Imagenet classification with deep convolutional neural networks, ... J. Lanchantin, R. Singh, Z. Lin and Y. Qi, Deep motif: Visualizing genomic sequence classifications ICLR Workshops 2016. Of course, this transcript was created with deep learning techniques largely automatically and only minor. Visualizing and Understanding Recurrent Networks. Recent work has also studied the contribution of feature vectors (Kim et al., 2017; Zhou et al., 2018b) or individual channels (Olah et al., 2018) to the final prediction. Improving performance of recurrent neural network with relu nonlinearity pdf. In the last part of my talk I’ll show how to exploit the partial order structure of the visual semantic hierarchy over words, sentences and images to learn order embeddings. A unit that correlates highly with an output object might not actually cause that output. To register and make hotel reservations, go here. Alternative Structures For Character-Level RNNs. David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, and Antonio Torralba. Later layers are dominated by low-level materials, edges and colors. Ablating successively larger sets of tree-causal units from a GAN trained on LSUN outdoor church images, showing that the more units are removed, the more trees are reduced, while buildings remain. We can explain individual network decisions using informative heatmaps (Zhou et al., 2018b, 2016; Selvaraju et al., 2017) or modified back-propagation (Simonyan et al., 2014; Bach et al., 2015; Sundararajan et al., 2017). Despite this tremendous success, many questions remain to be answered. Many problems in real-world applications involve predicting several random variables that are statistically related. Raquel Urtasun, University of Toronto. Visualizing and Understanding Convolutional Networks. In particular, windows are just as difficult to remove from a bedroom as tables and chairs from a conference room. Klaus-Robert Müller, and Wojciech Samek. west is the opposite of east, might limit the scalability of this work. Research Award at UCI, Best Thesis Award from the ACM SIGMETRICS For example, how to compose sentences from different-sized word vectors? The deeper intuition behind these two modifications is to utilize the existed yet unexplored information in the inner mechanism of RNN. Even if we try to add a door in layer4, that choice can be vetoed later if the object is not appropriate for the context. As an active research topic, many GAN variants have . statistics. Karpathy, Johnson, and Fei-Fei: Visualizing and Understanding Recurrent Networks, ICLR Workshop 2016 Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 10 - 58 May 4, 2017 They are as listed: This work is from NYU group, led by professor Yan LeCun. We've created a poll to gather feedback and suggestions on ICLR: https://www.facebook.com/events/1737067246550684/permalink/1737070989883643/. First, we compute the widely used Fréchet Inception Distance (Heusel et al., 2017) between the generated images and real images. ↩ An identical ”door” intervention at. We need go beyond words. at U.C.Irvine since While neural networks have acted as a strong unifying force in the design of modern AI systems, the neural network architectures themselves remain highly heterogeneous due to the variety of tasks to be solved. Julien Mairal, INRIA Prior work (Radford et al., 2016; Zhu et al., 2016) manipulates latent vectors and observes how the results change accordingly. Trevor Darrell, University of California, Berkeley Figure 14b shows a typical example, where a unit is devoted to letterboxing (white stripes at the top and bottom of images), but the segmentation has no confident label to assign to these. ICCV 2017; Karpathy et al., Visualizing and Understanding Recurrent Networks. Talks are now available on videolectures.net: http://videolectures.net/iclr2016_san_juan/. The silver medal is won by averaging vector + projection. On the contrary, in NASM, \(h\) is generated by \(p(h|q)\) in the supervised fashion. Found insideThis book is about making machine learning models and their decisions interpretable. Andrej Karpathy, Justin Johnson, Li Fei-Fei. ECCV 2014; Selvaraju et al., Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization. This work is an exntension of the famous blog, «The Unreasonable Effectiveness Of RNN» from Andrej Karpathy. However, the segmentation model sc can perform poorly in the cases where x does not resemble the original training set of sc. Convergent Learning: Do different neural networks learn the same representations? Overview: (a) A number of realistic outdoor church images generated by Progressive GANs. 2014: 1988-1996. Found inside – Page 342Stochastic pooling for regularization of deep convolutional neural networks. 1st International Conference on Learning Representations, ICLR 2013. Zeiler, M. D., & Fergus, R. (2014, September). Visualizing and understanding convolutional ... RNNs over tree 43; Recursive Neural Network (Socher+ 2011) 44 R Socher, J Pennington, ↩, Reasoning in Vector Space: An Exploratory Study of Question Answering. Then we quantify the spatial agreement between the unit u’s thresholded featuremap and a concept c’s segmentation with the following intersection-over-union (IoU) measure: where ∧ and ∨ denote intersection and union operations, and x=G(z) denotes the image generated from z. In particular, for any class from a universe of concepts c∈C, we seek to understand whether r explicitly represents c in some way where it is possible to factor r at locations P into two components. Experimental results show that RNNGs obtain better results in generating language than models that don’t exploit linguistic structures. For example, in Figure 18 we show dissections of layer4 representations of a Progressive GAN model trained on bedrooms, captured at a sequence of checkpoints during training. The five failure cases (where the segmentation is confident but rated as inaccurate by humans) arise from situations in which human evaluators saw one concept after observing only 20 top-activating images, while the algorithm, in evaluating 1000 images, counted a different concept as dominant. The output of the first convolutional layer has almost no units that match semantic objects, but many objects emerge at layers 4-7. Variational Autoencoders. A second AMT evaluation was done to rate the accuracy of both segmentation-derived and human-derived labels. Jiwei Li, Xinlei Chen, Eduard Hovy, and Dan Jurafsky. He obtained his PhD in Linguistics at the University of Maryland under Philip Resnik in 2010. The idea is straightforward. Very Deep Convolutional Networks for Large-Scale Image Recognition. For instructions on the submission process, go here. All layers are shown in Section S-6.7. Visualizing and Understanding Recurrent Networks pdf. We provide open source interpretation tools to help peer researchers and practitioners better understand their GAN models***Interactive demos, video, code, and data are available at GitHub and gandissect.csail.mit.edu.. Generative Adversarial Networks (GANs) (Goodfellow et al., 2014) have been able to produce photorealistic images, often indistinguishable from real images. Y. Ding, Y. Liu, H. Luan, M. Sun: Visualizing and Understanding Neural Machine Translation. We hope, you enjoy this as much as the videos. Another striking phenomenon is that many units represent parts of objects: for example, the conference room GAN contains separate units for the body and head of a person. 2014. of ICLR Workshop 2016. All these slides come from Ryan Kiros, one student of Professor Ruslan Salakhutdinov and in his share at CIFAR Neural Computation and Adaptive Perception Summer School 20145. Xiaolong Wang, Abhinav Shrivastava, and Abhinav Gupta. However, my concern is that, how to apply such embedding variant to longer semantic units? Visualizing and Understanding Recurrent Networks. arXiv: 1506.06579. Authors: Andrej Karpathy, Justin Johnson, Li Fei-Fei. It is implemented by a hybrid model with a char-RNN and word-RNN (W-RNN). activation vectors. Nicolas Le Roux, Criteo We optimize α over the following loss with an L2 regularization: where λ controls the relative importance of each term. Unified perceptual parsing for scene understanding. cluster weights, generate codebook, [quantize to codebook -> retrain codebook and repeat quantization] apply huffman coding. In the context of . To start with, the authors observe that the vanilla LSTM decomposes steps to prediction outputs, lacking of continuous global information, which will harms the coherence between the generated sequences. Visualizing and Understanding Recurrent Networks pdf. In Figure 9 we apply the method in Section 3.2 to identify sets of 20 units that have causal effects on common object classes in conference rooms scenes. Zhaopeng Tu . Found inside – Page 549Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS (2012) 7. ... In: ICLR (2013) Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. bracket and quote. Aaron Courville, Université de Montréal 2016. Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Brian Kingsbury, IBM Watson Group, Yoshua Bengio, Université de Montreal These networks are deployed in production on our customer fleet of 1M vehicles, where they . If you find a rendering bug, file an issue on GitHub. Both units are taken from a WGAN-GP for LSUN bedrooms. Prior visualization methods (Zeiler & Fergus, 2014; Bau et al., 2017; Karpathy et al., 2016) have brought new insights into CNN and RNNs research. "Self-Attention Generative Adversarial . Signature Verification using a "Siamese" Time Delay Neural Network ICLR 2020 . For question answering (selection), the model is called Neural Answer Selection Model (NASM) where \(h\) is for latent question semantics. If you look at what one of these higher layers is detecting, here I am showing the input image patches that are highly scored by one of the units. Applying our segmentation-based dissection method, 154/201 of these units are also labeled with a confident label with IoU > 0.05 by dissection. Found inside – Page 159Visualizing and Understanding Recurrent Networks. ICLR 2016, 2016. https://arxiv.org/abs/1506.02078. [Karpathy15b] Andrej Karpathy. The Unreasonable Effectiveness of Recurrent Neural Networks. This causality can be quantified by comparing the presence of trees in xi and xa and averaging effects over all locations and images. Units from. Our goal is to analyze how objects such as trees are encoded by the internal representations of a GAN generator G:z→x. tensor decomposition can be solved optimally using simple iterative (2016)) because the generator must represent all the information necessary to approximate the target distribution, while the discriminator only learns to capture the difference between real and fake images. Applied Perception in Graphics and Visualization 2004. Further work will be needed to understand the relationships between layers of a GAN. In this experiment, we insert these units by setting their activation to the fixed mean value for doors (further details in Section S-6.4). To further identify units that specialize in object parts, we expand each object class c into additional object part classes c-t, c-b, c-l, and c-r, which denote the top, bottom, left, or right half of the bounding box of a connected component. Deep Unordered Composition Rivals Syntactic Methods for Text Classification. She received her Why does one GAN variant work better than another? Joelle Pineau, McGill University We find that the GAN allows doors to be added in buildings, particularly in plausible locations such as where a window is present, or where bricks are present.
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