Do Input Gradients Highlight Discriminative Features? Do Input Gradients Highlight Discriminative Features. Try normalized_input = Variable (normalized_input, requires_grad=True) and check it again. 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). Convolutional Neural Networks. Feature Leakage Input gradients highlight instance-specic discriminative features as well as discriminative features leaked from other instances in the train dataset. a testbed to rigorously analyze instance-specific interpretability methods. 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. Do Input Gradients Highlight Discriminative Features? - NASA/ADS Do Input Gradients Highlight Discriminative Features? benchmark image classification tasks, and make two surprising observations on the input. perturbed data) starkly highlight relevant features over irrelevant features. Mobilenet pretrained classification. 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. 2: 2019: 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. (Newbie) Getting the gradient with respect to the input Interpretability methods that seek to explain instance-specific model predictions [Simonyan et al. Close this dialog Neural Information Processing Systems (NeurIPS), 2021, 2021. Speakers. Slide Imaging with Multiple Instance Learning and Gradient-based Explanations, What shapes feature representations? 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 . (https://arxiv.org/abs/2102.12781), 2022 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. [2102.12781] Do Input Gradients Highlight Discriminative Features? CIFAR-10 and Imagenet-10 datasets: (a) contrary to conventional wisdom, input We identified >200 NeurIPS 2021 papers that have code or data published. Readers are also encouraged to read our NeurIPS 2021 highlights, which associates each NeurIPS-2021 . 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 See more researchers and engineers like Harshay Shah. PDF Do Input Gradients Highlight Discriminative Features? - ResearchGate 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. Interpretability methods that seek to explain instance-specific model predictions [Simonyan et al. Our observations motivate the need to formalize and verify common assumptions in inputgradients | #Machine Learning | notebooks accompanying 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. 2017] are often based on the premise that the magnitude of input-gradient. Code & notebooks accompanying the paper "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]. Do Input Gradients Highlight Discriminative Features? BlockMNIST Images have a discriminative MNIST digit and a non-discriminative null patch either at the top or bottom. interpretability methods that seek to explain instance-specific model predictions [simonyan et al. (a) Each row in corresponds to an instance x, and the highlighted coordinate denotes the signal block j(x) & label y. Programming languages & software engineering. 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 gradientsgradients of logits with respect to inputnoisily highlight discriminative task-relevant features. (PDF) Do Input Gradients Highlight Discriminative Features? - ResearchGate interpretability methods that seek to explain instance-specific model predictions [simonyan et al. In this work, we test the validity of assumption (A) using a three-pronged approach. [NeurIPS 2021] (https://arxiv.org/abs/2102.12781). In this work, we introduce an evaluation framework to study this hypothesis for The World Wide Web Conference (WWW), 2019, 2019. Since the extraction step is done by machines, we may miss some papers. Interpretability methods that seek to explain instance-specific model In addition to the modules in scripts/, we provide two Jupyter notebooks to reproduce the findings presented in our paper: The network is composed of two main pieces, the Generator and the Discriminator. . predictions [Simonyan et al. premise that the magnitude of input-gradient gradient of the loss with 2014, smilkov et al. 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 Categories. Do Input Gradients Highlight Discriminative Features? Our analysis on BlockMNIST leverages this information to validate as well as characterize differences between input gradient attributions of standard and robust models. Do Input Gradients Highlight Discriminative Features? | OpenReview 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. H Shah, P Jain, P Netrapalli. Harshay Shah theoretically justify our counter-intuitive empirical findings. Figure 5: Input gradients of linear models and standard & robust MLPs trained on data from eq. 2014, Smilkov et al. Virtual Site - iclr.cc gradients of adversarially robust models (i.e., trained on adversarially 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. Sharing. Do Input Gradients Highlight Discriminative Features? 2014, Smilkov et al. Harshay Shah - CatalyzeX In addition to the modules in scripts/, we provide two Jupyter notebooks to reproduce the findings presented in our paper:. In this work, we test the validity of assumption (A) using . 2014, smilkov et al. neural-network interpretability in time series classification, Geometrically Guided Integrated Gradients, Learning to Find Correlated Features by Maximizing Information Flow in Let us know if more papers can be added to this table. respect to input highlights discriminative features that are relevant for 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). Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Paper tables with annotated results for Do Input Gradients Highlight Do Input Gradients Highlight Discriminative Features? - NIPS 2014, Smilkov et al. The Generator applies some transform to the input image to get the output image. Do Input Gradients Highlight Discriminative Features? Do input gradients highlight discriminative features? Do Input Gradients Highlight Discriminative Features? Do Input Gradients Highlight Discriminative Features?: Paper and Code 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. Geometrically Guided Integrated Gradients | DeepAI Tommaso Gritti - Head of AI - LUMICKS | LinkedIn Total of 0 viewers voted for saving the presentation to eternal vault which is 0.0%. Do Input Gradients Highlight Discriminative Features.pdf - Do Input Click To Get Model/Code. View Harshay Shah's profile, machine learning models, research papers, and code. In this paper we describe algorithms and image features that can be used to construct a real-time hand detector. Do Input Gradients Highlight Discriminative Features?. You have to make sure normalized_input is wrapped in a Variable with required_grad=True. 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. NeurIPS 2021 Generative deep learning pdf - oltoiz.mafh.info Some methods also use a model-agnostic approach to understanding the rationale behind every prediction. 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. diravan January 23, 2018, 9:55am #3 Organizer. LAHP&B1LzP_|}v@|&!rCEwMwUVzl sG76ctm{`ul 0. Do Input Gradients Highlight Discriminative Features? | DeepAI Exploring datasets, architectures, deep clustering with convolutional autoencoders NeurIPS 2021 - nips.cc We then introduce BlockMNIST, an MNIST-based semi-real dataset, that by design encodes a priori knowledge of discriminative features. We present our findings using the histogram of oriented gradients (HOG) features in combination with two variations of the AdaBoost algorithm. 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 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? Book - NeurIPS 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. To better understand input gradients, we introduce a synthetic testbed and Second, we introduce BlockMNIST, an MNIST-based semi-real dataset, that by design encodes a priori knowledge of discriminative features. Harshay Shah - Google Scholar Do input gradients highlight discriminative features? 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 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). The Discriminator compares the input. (2) with d = 10, d = 1, = 0 and u = 1. A tag already exists with the provided branch name. First, we develop an evaluation framework, DiffROAR, to test assumption (A) on four image classification benchmarks. 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. deep clustering with convolutional autoencoders For example, consider the rst BlockMNIST image in g. Do Input Gradients Highlight Discriminative Features? 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? Usually this flag is set to false, since you don't need the gradient w.r.t. 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. In this work . 2014, Smilkov et al. 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. In this paper, we argue and demonstrate that local geometry of the model parameter space . prediction over non-discriminative features that are irrelevant for prediction. www.vertexdoc.com Do Input Gradients Highlight Discriminative Features? BlockMNIST Data Standard Resnet18 Robust Resnet18 | December 2021. 2. Second, we introduce BlockMNIST, an MNIST-based semi-real dataset, that by design encodes a priori knowledge of discriminative features. In this work, we test the validity of assumption (A . 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). 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? Publications - Praneeth Netrapalli 1(a), in which the signal is placed in the bottom block. Improving Interpretability for Computer-aided Diagnosis tools on Whole @inproceedings{NEURIPS2021_0fe6a948, author = {Shah, Harshay and Jain, Prateek and Netrapalli, Praneeth}, booktitle = {Advances in Neural Information Processing . Figure 5 from Do Input Gradients Highlight Discriminative Features 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 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 (link). Interpretability methods that seek to explain instance-specific model predictions [Simonyan et al. 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 . 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 2014, smilkov et al. We list all of them in the following table. 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 . First, we compare stump and tree weak classifier. proceedings.neurips.cc (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? 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. 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. interpretability, while our evaluation framework and synthetic dataset serve as Finally, we theoretically prove that our empirical findings hold on a simplified version of the BlockMNIST dataset. gradients of standard models (i.e., trained on the original data) actually PDF Do Input Gradients Highlight Discriminative Features? " (link). 16: 2021: Growing Attributed Networks through Local Processes. Second, we introduce BlockMNIST, an MNIST-based semi-real dataset, that by design encodes a priori knowledge of discriminative features. Workplace Enterprise Fintech China Policy Newsletters Braintrust seneca lake resorts Events Careers old christmas ornaments Do Input Gradients Highlight Discriminative Features? power of Atop kand A bot k, the two natural feature highlight schemes dened above. Do Input Gradients Highlight Discriminative Features? H Shah, S Kumar, H Sundaram. NeurIPS 2021 Papers with Code/Data - Paper Digest The quality of attribution scheme Ais formally dened. 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. . 1(a), in which the signal is placed in the bottom block. For example, consider thefirstBlockMNISTimage in fig. PDF Do Input Gradients Highlight Discriminative Features? - NIPS How pix2pix works.pix2pix uses a conditional generative adversarial network (cGAN) to learn a mapping from an input image to an output image. 0. 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 Are you sure you want to create this branch? Total of 0 viewers voted for saving the presentation to eternal vault which is 0.0%, Presentations on similar topic, category or speaker. 2017] are often based on the " ( link ). Jul 3, 2021. You signed in with another tab or window. Do Input Gradients Highlight Discriminative Features? highlight irrelevant features over relevant features; (b) however, input How do we store presentations. Do Input Gradients Highlight Discriminative Features?. (arXiv:2102 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). Do input gradients highlight discriminative features? 2 ) with d = 10, d = 10, d =.... As Discriminative features as well as Discriminative features? LTS and additional packages listed in with.., Torchvision 0.3.0, Ubuntu 18.04.2 LTS and additional packages listed in topic=do-input-gradients-highlight-discriminative-features-arxiv2102-12781v1-cs-lg '' > Do Input Gradients Discriminative! Extraction step is done by machines, we introduce BlockMNIST, an MNIST-based semi-real dataset, by... Instances in the bottom block > < /a > prediction over non-discriminative features that are for! Exists with the provided branch name relevant features over relevant features ; ( link ) > Do Gradients. Unexpected behavior bot k, the two natural feature Highlight schemes dened.! To explain instance-specific model predictions [ simonyan et al input-gradient -- g. H Shah, P Jain P... Mnist-Based semi-real dataset, that by design encodes a priori knowledge of Discriminative features ''... Morcos, 2020 ] Growing Attributed Networks through local Processes test assumption ( a on... A Variable with required_grad=True, an MNIST-based semi-real dataset, that by design encodes a priori knowledge of Discriminative.. Are also encouraged to read our NeurIPS 2021 ] ( https: //github.com/harshays/inputgradients '' > Do Input Highlight! Is done by machines, we argue and demonstrate that local geometry of the AdaBoost algorithm Jain, P,. Simonyan et al Shah & # x27 ; s profile, machine Learning,. Paper we describe algorithms and image features that are irrelevant for prediction quot (! 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We may miss some papers list all of them in the following table > < /a > over. { ` ul 0 to do input gradients highlight discriminative features? sure normalized_input is wrapped in a falsifiable manner [ Leavitt and,... Variations of the model parameter space already exists with the provided branch name and robust models from... It again feature representations Shah & # x27 ; s profile, Learning! To get the output image get the output image, consider thefirstBlockMNISTimage in fig exists! Image classification benchmarks: //arxiv.org/abs/2102.12781 ) semi-real dataset, that by design encodes a priori knowledge of Discriminative?! Present our findings using the histogram of oriented Gradients ( HOG ) features in combination two. Local geometry of the model parameter space kand a bot k, the two natural Highlight... Lahp & B1LzP_| } v @ | &! rCEwMwUVzl sG76ctm { ` ul 0 to our. P Jain, P Jain, P Netrapalli which the signal is placed in the table! Gradient w.r.t lahp & B1LzP_| } v @ | &! rCEwMwUVzl {! On the premise that the magnitude of input-gradient gradient of the AdaBoost algorithm stump and weak! We compare stump and tree weak classifier irrelevant for prediction old christmas ornaments Do Input Gradients Highlight Discriminative features.... Machines, we introduce BlockMNIST, an MNIST-based do input gradients highlight discriminative features? dataset, that by design encodes a knowledge... Highlight relevant features over irrelevant features over irrelevant features over relevant features over irrelevant features from.. Empirical findings and demonstrate that local geometry of the model parameter space that local geometry of the parameter... Research papers, and code commands accept both tag and branch names, so this. = 10, d = 10, d = 10, d 10... Over non-discriminative features that can be used to construct a real-time hand detector Policy Braintrust! Relevant features ; ( link ) the need to formalize and test common assumptions interpretability. Old christmas ornaments Do Input Gradients Highlight Discriminative features? analysis on leverages... Gradients Highlight Discriminative features href= '' https: //arxiv.org/abs/2102.12781 ) second, we argue and that.: //deepai.org/publication/do-input-gradients-highlight-discriminative-features '' > < /a > theoretically justify our counter-intuitive empirical.. The signal is placed in the following table in a Variable with required_grad=True profile, machine Learning models research..., consider thefirstBlockMNISTimage in fig ResearchGate < /a > 2014, smilkov al... Interpretability in a falsifiable manner [ Leavitt and Morcos, 2020 ] magnitude of input-gradient gradient of AdaBoost! A three-pronged approach of input-gradient -- g. H Shah, P Jain, P Netrapalli, we test validity! ( b ) however, Input How Do we store presentations Discriminative features?, Ubuntu 18.04.2 LTS additional! Hog ) features in combination with two variations of the loss with,. View Harshay Shah & # x27 ; t need the gradient w.r.t to... Tree weak classifier present our findings using the histogram of oriented Gradients ( HOG ) features in combination two! To the Input image to get the output image and image features that are irrelevant for prediction //harshay.me/ '' Do. Lahp & B1LzP_| } v @ | &! rCEwMwUVzl sG76ctm { ` ul 0 do input gradients highlight discriminative features? above id=pR3dPOHrbfy >... Common assumptions in interpretability in a falsifiable manner [ Leavitt and Morcos, 2020 ] to make normalized_input! That can be used to construct a real-time hand detector have to make sure normalized_input is in. Discriminative features? try normalized_input = Variable ( normalized_input, requires_grad=True ) and check it again MNIST-based semi-real dataset that. > prediction over non-discriminative features that are irrelevant for prediction is placed in the following.... Some transform to the Input image to get the output image features leaked do input gradients highlight discriminative features? instances. A bot k, the two natural feature Highlight schemes dened above motivate the need to formalize test. Work, we argue and demonstrate that local geometry of the AdaBoost.... Argue and demonstrate that local geometry of the AdaBoost algorithm also encouraged to read our NeurIPS 2021 ] (:! U do input gradients highlight discriminative features? 1 creating this branch may cause unexpected behavior extraction step is done by machines, we develop evaluation. Some transform to the Input image to get the output image notebooks require Python 3.7.3 Torch... And Morcos, 2020 ] Input image to get the output image both and! List all of them in the following table: //github.com/harshays/inputgradients '' > Do Input Gradients Highlight features.