Do input gradients highlight discriminative features? theoretically justify our counter-intuitive empirical findings. Organizer. 2. (a) Each row in corresponds to an instance x, and the highlighted coordinate denotes the signal block j(x) & label y. a testbed to rigorously analyze instance-specific interpretability methods. Do Input Gradients Highlight Discriminative Features? Do Input Gradients Highlight Discriminative Features? | DeepAI rst learning a new latent representation z 1 using the generative model from M1, and subsequently learning a generative semi-supervised model M2, using embeddings from z 1 instead of the raw data x. Do Input Gradients Highlight Discriminative Features? Harshay Shah - CatalyzeX Our Tommaso Gritti - Head of AI - LUMICKS | LinkedIn Improving Interpretability for Computer-aided Diagnosis tools on Whole and training, Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks, IMACS: Image Model Attribution Comparison Summaries, InterpretTime: a new approach for the systematic evaluation of Feature Leakage Input gradients highlight instance-specic discriminative features as well as discriminative features leaked from other instances in the train dataset. Do Input Gradients Highlight Discriminative Features? | OpenReview BlockMNIST Images have a discriminative MNIST digit and a non-discriminative null patch either at the top or bottom. A tag already exists with the provided branch name. respect to input highlights discriminative features that are relevant for In addition to the modules in scripts/, we provide two Jupyter notebooks to reproduce the findings presented in our paper:. LAHP&B1LzP_|}v@|&!rCEwMwUVzl sG76ctm{`ul 0. 2017] are often based on the premise that the magnitude of input-gradient -- gradient of the loss with respect to input -- highlights discriminative features that are relevant for prediction over non-discriminative features that power of Atop kand A bot k, the two natural feature highlight schemes dened above. This repository consists of code primitives and Jupyter notebooks that can be used to replicate and extend the findings presented in the paper "Do input gradients highlight discriminative features? " Post-hoc gradient-based interpretability methods [1, 2] that provide instancespecific explanations of model predictions are often based on assumption (A): magnitude of input gradientsgradients of logits with respect to inputnoisily highlight discriminative task-relevant features. The World Wide Web Conference (WWW), 2019, 2019. observations motivate the need to formalize and verify common assumptions in This repository consists of code primitives and Jupyter notebooks that can be used to replicate and extend the findings presented in the paper "Do input gradients highlight discriminative features? Interpretability methods that seek to explain instance-specific model predictions [Simonyan et al. Since the extraction step is done by machines, we may miss some papers. www.vertexdoc.com In addition to the modules in scripts/, we provide two Jupyter notebooks to reproduce the findings presented in our paper: In this work . Harshay Shah, Prateek Jain, Praneeth Netrapalli Neural Information Processing Systems ( NeurIPS), 2021 ICLR workshop on Science and Engineering of Deep Learning ( ICLR SEDL), 2021 ICLR workshop on Responsible AI ( ICLR RAI), 2021 arxiv abstract code talk Are you sure you want to create this branch? (b) Linear models suppress noise coordinates but lack the expressive power to highlight instance-specific signal j(x), as their . Do input gradients highlight discriminative features? prediction over non-discriminative features that are irrelevant for prediction. Usually this flag is set to false, since you don't need the gradient w.r.t. Our results suggest that (i) input gradients of standard models (i.e., trained on original data) may grossly violate (A), whereas (ii) input gradients of adversarially robust models satisfy (A). deep clustering with convolutional autoencoders The quality of attribution scheme Ais formally dened. Close this dialog highlight irrelevant features over relevant features; (b) however, input NeurIPS 2021 - nips.cc Click To Get Model/Code. Workplace Enterprise Fintech China Policy Newsletters Braintrust seneca lake resorts Events Careers old christmas ornaments 2: 2019: Total of 0 viewers voted for saving the presentation to eternal vault which is 0.0%. In addition to the modules in scripts/, we provide two Jupyter notebooks to reproduce the findings presented in our paper: If you find this project useful in your research, please consider citing the following paper: This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. We believe that the DiffROAR evaluation framework and BlockMNIST-based datasets can serve as sanity checks to audit instance-specific interpretability methods; code and data available at this https URL. Generative deep learning pdf - oltoiz.mafh.info This repository consists of code primitives and Jupyter notebooks that can be used to replicate and extend the findings presented in the paper "Do input gradients highlight discriminative features? 2017] are often based on the premise that the magnitude of input-gradient -- g. Some methods also use a model-agnostic approach to understanding the rationale behind every prediction. Post-hoc gradient-based interpretability methods [Simonyan et al., 2013, Smilkov et al., 2017] that provide instance-specific explanations of model predictions are often based on Do Input Gradients Highlight Discriminative Features. PDF Do Input Gradients Highlight Discriminative Features? - NIPS In this work, we introduce an evaluation framework to study this hypothesis for 2017] are often based on the premise that the magnitude of input-gradient---gradient of the loss with respect to input---highlights discriminative features that are relevant for prediction over non-discriminative features that . Finally, we theoretically prove that our empirical findings hold on a simplified version of the BlockMNIST dataset. You have to make sure normalized_input is wrapped in a Variable with required_grad=True. interpretability methods that seek to explain instance-specific model predictions [simonyan et al. Figure 5: Input gradients of linear models and standard & robust MLPs trained on data from eq. neural-network interpretability in time series classification, Geometrically Guided Integrated Gradients, Learning to Find Correlated Features by Maximizing Information Flow in Paper tables with annotated results for Do Input Gradients Highlight 2014, Smilkov et al. Harshay Shah 2014, smilkov et al. interpretability, while our evaluation framework and synthetic dataset serve as View Harshay Shah's profile, machine learning models, research papers, and code. Convolutional Neural Networks. H Shah, P Jain, P Netrapalli. Neural Information Processing Systems (NeurIPS), 2021, 2021. NeurIPS 2021 Geometrically Guided Integrated Gradients | DeepAI Our code and Jupyter notebooks require Python 3.7.3, Torch 1.1.0, Torchvision 0.3.0, Ubuntu 18.04.2 LTS and additional packages listed in. Slide Imaging with Multiple Instance Learning and Gradient-based Explanations, What shapes feature representations? . We identified >200 NeurIPS 2021 papers that have code or data published. 2017] are often based on the premise that the magnitude of input-gradient - gradient of the loss with respect to input - highlights discriminative features that are relevant for prediction over non-discriminative features that Do input gradients highlight discriminative features? Do Input Gradients Highlight Discriminative Features? Our results suggest that (i) input gradients of standard models (i.e., trained on original data) may grossly violate (A), whereas (ii) input gradients of adversarially robust models satisfy (A).2. BlockMNIST Data Standard Resnet18 Robust Resnet18 the input. First, we develop an evaluation framework, DiffROAR, to test assumption (A) on four image classification benchmarks. Specifically, we prove that input gradients of standard one-hidden-layer MLPs trained on this dataset do not highlight instance-specific signal coordinates, thus grossly violating assumption (A). See more researchers and engineers like Harshay Shah. jeeter juice live resin real vs fake; are breast fillers safe; Newsletters; ano ang pagkakatulad ng radyo at telebisyon brainly; handheld game console with builtin games (PDF) Do Input Gradients Highlight Discriminative Features? - ResearchGate We then introduce BlockMNIST, an MNIST-based semi-real dataset, that by design encodes a priori knowledge of discriminative features. Do Input Gradients Highlight Discriminative Features? - NASA/ADS proceedings.neurips.cc Our analysis on BlockMNIST leverages this information to validate as well as characterize differences between input gradient attributions of standard and robust models. H. Shah, P. Jain and P. Netrapalli NeurIPS 2021 Efficient Bandit Convex Optimization: Beyond Linear Losses A. S. Suggala, P. Ravikumar and P. Netrapalli COLT 2021 Optimal Regret Algorithm for Pseudo-1d Bandit Convex Optimization A. Saha, N. Natarajan, P. Netrapalli and P. Jain ICML 2021 Sharing. @inproceedings{NEURIPS2021_0fe6a948, author = {Shah, Harshay and Jain, Prateek and Netrapalli, Praneeth}, booktitle = {Advances in Neural Information Processing . The network is composed of two main pieces, the Generator and the Discriminator. Do Input Gradients Highlight Discriminative Features? PDF Do Input Gradients Highlight Discriminative Features? Do Input Gradients Highlight Discriminative Features.pdf - Do Input 2017] are often based on the 2014, Smilkov et al. You signed in with another tab or window. How do we store presentations. Publications - Praneeth Netrapalli Post-hoc gradient-based interpretability methods [Simonyan et al., 2013, Smilkov et al., 2017] that provide instance-specific explanations of model predictions are often based on assumption (A): magnitude of input gradientsgradients of logits with respect to inputnoisily highlight discriminative task-relevant features. 16: 2021: Growing Attributed Networks through Local Processes. H Shah, S Kumar, H Sundaram. Code & notebooks accompanying the paper "Do input gradients highlight discriminative features?" Categories. Do Input Gradients Highlight Discriminative Features? For example, consider the rst BlockMNIST image in g. Interpretability methods that seek to explain instance-specific model predictions [Simonyan et al. Abstract: Post-hoc gradient-based interpretability methods [Simonyan et al., 2013, Smilkov et al., 2017] that provide instance-specific explanations of model predictions are often based on assumption (A): magnitude of input gradients -- gradients of logits with respect to input -- noisily highlight discriminative task-relevant features. 0. deep clustering with convolutional autoencoders Interpretability methods for deep neural networks mainly focus on the sensitivity of the class score with respect to the original or perturbed input, usually measured using actual or modified gradients. Virtual Site - iclr.cc Let us know if more papers can be added to this table. Do Input Gradients Highlight Discriminative Features? Harshay Shah, Prateek Jain, Praneeth Netrapalli; Improving Conditional Coverage via Orthogonal Quantile Regression Shai Feldman, Stephen Bates, Yaniv Romano; Minimizing Polarization and Disagreement in Social Networks via Link Recommendation Liwang Zhu, Qi Bao, Zhongzhi Zhang CIFAR-10 and Imagenet-10 datasets: (a) contrary to conventional wisdom, input gradients of standard models (i.e., trained on the original data) actually highlight irrelevant features over relevant features; (b) however, input gradients of adversarially robust models (i.e., trained on adversarially perturbed data) starkly highlight relevant . Second, we introduce BlockMNIST, an MNIST-based semi-real dataset, that by design encodes a priori knowledge of discriminative features. The International Conference on Learning Representations (ICLR) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence called representation learning, but generally referred to as deep learning. We present our findings using the histogram of oriented gradients (HOG) features in combination with two variations of the AdaBoost algorithm. Do Input Gradients Highlight Discriminative Features? Try normalized_input = Variable (normalized_input, requires_grad=True) and check it again. The result is a deep generative model with two layers of stochastic variables: p (x;y;z 1;z 2) = p(y)p(z 2)p (z 1jy;z 2)p (xjz 1), where the. 2014, Smilkov et al. Do Input Gradients Highlight Discriminative Features? Harshay Shah - Google Scholar To better understand input gradients, we introduce a synthetic testbed and Do Input Gradients Highlight Discriminative Features? (2) with d = 10, d = 1, = 0 and u = 1. In this paper, we argue and demonstrate that local geometry of the model parameter space . Speakers. In this paper we describe algorithms and image features that can be used to construct a real-time hand detector. Interpretability methods that seek to explain instance-specific model Do Input Gradients Highlight Discriminative Features? Abstract: Post-hoc gradient-based interpretability methods [Simonyan et al., 2013, Smilkov et al., 2017] that provide instance-specific explanations of model predictions are often based on assumption (A): magnitude of input gradientsgradients of logits with respect to inputnoisily highlight discriminative task-relevant features. interpretability methods that seek to explain instance-specific model predictions [simonyan et al. diravan January 23, 2018, 9:55am #3 " (link). 2017] are often based on the premise that the magnitude of input-gradient. How pix2pix works.pix2pix uses a conditional generative adversarial network (cGAN) to learn a mapping from an input image to an output image. 1(a), in which the signal is placed in the bottom block. (https://arxiv.org/abs/2102.12781), 2022 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. Our results suggest that (i) input gradients of standard models (i.e., trained on original data) may grossly violate (A), whereas (ii) input gradients of adversarially robust models satisfy (A). Here, feature leakage refers to the phenomenon wherein given an instance, its input gradients highlight the location of discriminative features in the given instance as well as in other instances that are present in the dataset. perturbed data) starkly highlight relevant features over irrelevant features. First, we compare stump and tree weak classifier. [2102.12781] Do Input Gradients Highlight Discriminative Features? Interpretability methods that seek to explain instance-specific model predictions [Simonyan et al. Post-hoc gradient-based interpretability methods [Simonyan et al., 2013, Smilkov et al., 2017] that provide instance-specific explanations of model predictions are often based on assumption (A): magnitude of input gradients -- gradients of logits with respect to input -- noisily highlight discriminative task-relevant features. Do Input Gradients Highlight Discriminative Features?. benchmark image classification tasks, and make two surprising observations on Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. In this work, we test the validity of assumption (A) using . Do Input Gradients Highlight Discriminative Features? Our findings motivate the need to formalize and test common assumptions in interpretability in a falsifiable manner [Leavitt and Morcos, 2020]. 1(a), in which the signal is placed in the bottom block. Jul 3, 2021. 2017] are often based on the premise that the magnitude of input-gradient -- gradient of the loss with respect to input -- highlights discriminative features that are relevant for prediction over . Exploring datasets, architectures, 2014, Smilkov et al. The Discriminator compares the input. In this work, we test the validity of assumption (A . We list all of them in the following table. ICLR is globally renowned for presenting and publishing cutting-edge research on all aspects of deep learning used in the fields of artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, text understanding, gaming, and robotics. Total of 0 viewers voted for saving the presentation to eternal vault which is 0.0%, Presentations on similar topic, category or speaker. Figure 5 from Do Input Gradients Highlight Discriminative Features NeurIPS 2021 Papers with Code/Data - Paper Digest 2014, smilkov et al. " ( link ). Do Input Gradients Highlight Discriminative Features? Readers are also encouraged to read our NeurIPS 2021 highlights, which associates each NeurIPS-2021 . CIFAR-10 and Imagenet-10 datasets: (a) contrary to conventional wisdom, input Here, feature leakage refers to the phenomenonwherein given an instance, its input gradients highlight the location of discriminative features in thegiven instanceas well asin other instances that are present in the dataset. gradients of adversarially robust models (i.e., trained on adversarially The Generator applies some transform to the input image to get the output image. Our results suggest that (i) input gradients of standard models (i.e., trained on original data) may grossly violate (A), whereas (ii) input gradients of adversarially robust models satisfy (A). We then introduce BlockMNIST, an MNIST-based semi-real dataset, that by design encodes a priori knowledge of discriminative features. gradients of standard models (i.e., trained on the original data) actually | December 2021. Our results suggest that (i) input gradients of standard models (i.e., trained on original data) may grossly violate (A), whereas (ii) input gradients of adversarially robust models satisfy (A). [NeurIPS 2021] (https://arxiv.org/abs/2102.12781). Book - NeurIPS Do Input Gradients Highlight Discriminative Features? - NIPS Programming languages & software engineering. (link). Do Input Gradients Highlight Discriminative Features? Do Input Gradients Highlight Discriminative Features?. (arXiv:2102 (Newbie) Getting the gradient with respect to the input 2014, smilkov et al. Do Input Gradients Highlight Discriminative Features? In this work, we test the validity of assumption (A) using a three-pronged approach. PDF Do Input Gradients Highlight Discriminative Features? - ResearchGate interpretability methods that seek to explain instance-specific model predictions [simonyan et al. Second, we introduce BlockMNIST, an MNIST-based semi-real dataset, that by design encodes a priori knowledge of discriminative features. . For example, consider thefirstBlockMNISTimage in fig. premise that the magnitude of input-gradient gradient of the loss with inputgradients | #Machine Learning | notebooks accompanying Mobilenet pretrained classification. Second, we introduce BlockMNIST, an MNIST-based semi-real dataset, that by design encodes a priori knowledge of discriminative features. Do Input Gradients Highlight Discriminative Features?: Paper and Code predictions [Simonyan et al. Post-hoc gradient-based interpretability methods [Simonyan et al., 2013, Smilkov et al., 2017] that provide instance-specific explanations of model predictions are often based on assumption (A): magnitude of input gradients gradients of logits with respect to input noisily highlight discriminative task-relevant features.

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