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 gradients -- gradients of logits with respect to input -- noisily highlight discriminative task-relevant features. Second, we introduce BlockMNIST, an MNIST-based semi-real dataset, that by design encodes a priori knowledge of discriminative features. Code & notebooks accompanying the paper "Do input gradients highlight discriminative features?" interpretability methods that seek to explain instance-specific model predictions [simonyan et al. a testbed to rigorously analyze instance-specific interpretability methods. 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 . 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. 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 In this work . " (link). 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. 2017] are often based on the premise that the magnitude of input-gradient. Readers are also encouraged to read our NeurIPS 2021 highlights, which associates each NeurIPS-2021 . First, we compare stump and tree weak classifier. 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. observations motivate the need to formalize and verify common assumptions in Usually this flag is set to false, since you don't need the gradient w.r.t. BlockMNIST Data Standard Resnet18 Robust Resnet18 predictions [Simonyan et al. 2014, smilkov et al. 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. | December 2021. the input. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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? Speakers. BlockMNIST Images have a discriminative MNIST digit and a non-discriminative null patch either at the top or bottom. Second, we 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 Do Input Gradients Highlight Discriminative Features? Let us know if more papers can be added to this table. Our findings motivate the need to formalize and test common assumptions in interpretability in a falsifiable manner [Leavitt and Morcos, 2020]. Are you sure you want to create this branch? We then introduce BlockMNIST, an MNIST-based semi-real dataset, that by design encodes a priori knowledge of discriminative features. Total of 0 viewers voted for saving the presentation to eternal vault which is 0.0%. 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 Do input gradients highlight discriminative features? In this work, we test the validity of assumption (A) using a three-pronged approach. We identified >200 NeurIPS 2021 papers that have code or data published. 2. In this work, we test the validity of assumption (A) using . neural-network interpretability in time series classification, Geometrically Guided Integrated Gradients, Learning to Find Correlated Features by Maximizing Information Flow in We present our findings using the histogram of oriented gradients (HOG) features in combination with two variations of the AdaBoost algorithm. Since the extraction step is done by machines, we may miss some papers. 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. 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 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. (link). Jul 3, 2021. Try normalized_input = Variable (normalized_input, requires_grad=True) and check it again. 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. 1(a), in which the signal is placed in the bottom block. interpretability methods that seek to explain instance-specific model predictions [simonyan et al. First, we develop an evaluation framework, DiffROAR, to test assumption (A) on four image classification benchmarks. Do Input Gradients Highlight Discriminative Features. For example, consider thefirstBlockMNISTimage in fig. Do Input Gradients Highlight Discriminative Features?. 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 power of Atop kand A bot k, the two natural feature highlight schemes dened above. diravan January 23, 2018, 9:55am #3 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. 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. View Harshay Shah's profile, machine learning models, research papers, and code. 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? In this paper we describe algorithms and image features that can be used to construct a real-time hand detector. 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? " Convolutional Neural Networks. You have to make sure normalized_input is wrapped in a Variable with required_grad=True. 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. Total of 0 viewers voted for saving the presentation to eternal vault which is 0.0%, Presentations on similar topic, category or speaker. 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). How do we store presentations. 16: 2021: Growing Attributed Networks through Local Processes. 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 . interpretability methods that seek to explain instance-specific model predictions [simonyan et al. NeurIPS 2021 2017] are often based on the premise that the magnitude of input-gradient -- g. 2014, smilkov et al. prediction over non-discriminative features that are irrelevant for prediction. 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). Exploring datasets, architectures, Workplace Enterprise Fintech China Policy Newsletters Braintrust seneca lake resorts Events Careers old christmas ornaments The quality of attribution scheme Ais formally dened. Neural Information Processing Systems (NeurIPS), 2021, 2021. 2014, smilkov et al. Interpretability methods that seek to explain instance-specific model 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. CIFAR-10 and Imagenet-10 datasets: (a) contrary to conventional wisdom, input (b) Linear models suppress noise coordinates but lack the expressive power to highlight instance-specific signal j(x), as their . (https://arxiv.org/abs/2102.12781), 2022 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. 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 [NeurIPS 2021] (https://arxiv.org/abs/2102.12781). benchmark image classification tasks, and make two surprising observations on . In this work, we introduce an evaluation framework to study this hypothesis for We then introduce BlockMNIST, an MNIST-based semi-real dataset, that by design encodes a priori knowledge of discriminative features. Organizer. The Generator applies some transform to the input image to get the output image. 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. 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 The Discriminator compares the input. See more researchers and engineers like Harshay Shah. Do input gradients highlight discriminative features? Improving Interpretability for Computer-aided Diagnosis tools on Whole For example, consider the rst BlockMNIST image in g. Finally, we theoretically prove that our empirical findings hold on a simplified version of the BlockMNIST dataset. respect to input highlights discriminative features that are relevant for theoretically justify our counter-intuitive empirical findings. Do Input Gradients Highlight Discriminative Features? premise that the magnitude of input-gradient gradient of the loss with Mobilenet pretrained classification. The World Wide Web Conference (WWW), 2019, 2019. 2014, Smilkov et al. A tag already exists with the provided branch name. 2014, Smilkov et al. Sharing. 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. Some methods also use a model-agnostic approach to understanding the rationale behind every prediction. (2) with d = 10, d = 1, = 0 and u = 1. . " ( link ). 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). interpretability, while our evaluation framework and synthetic dataset serve as 0. We list all of them in the following table. Do Input Gradients Highlight Discriminative Features? How pix2pix works.pix2pix uses a conditional generative adversarial network (cGAN) to learn a mapping from an input image to an output image. 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. Our Categories. 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. 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? 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 . 2014, Smilkov 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. Click To Get Model/Code. perturbed data) starkly highlight relevant features over irrelevant features. To better understand input gradients, we introduce a synthetic testbed and 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 In addition to the modules in scripts/, we provide two Jupyter notebooks to reproduce the findings presented in our paper: 2: 2019: Programming languages & software engineering. gradients of adversarially robust models (i.e., trained on adversarially 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. @inproceedings{NEURIPS2021_0fe6a948, author = {Shah, Harshay and Jain, Prateek and Netrapalli, Praneeth}, booktitle = {Advances in Neural Information Processing . In this work, we test the validity of assumption (A . 2017] are often based on the 1(a), in which the signal is placed in the bottom block. 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). 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? H Shah, P Jain, P Netrapalli. In this paper, we argue and demonstrate that local geometry of the model parameter space . You signed in with another tab or window. Feature Leakage Input gradients highlight instance-specic discriminative features as well as discriminative features leaked from other instances in the train dataset. LAHP&B1LzP_|}v@|&!rCEwMwUVzl sG76ctm{`ul 0. 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). H Shah, S Kumar, H Sundaram. Do Input Gradients Highlight Discriminative Features? 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. (a) Each row in corresponds to an instance x, and the highlighted coordinate denotes the signal block j(x) & label y. highlight irrelevant features over relevant features; (b) however, input Interpretability methods that seek to explain instance-specific model predictions [Simonyan et al. 2014, Smilkov et al. Our analysis on BlockMNIST leverages this information to validate as well as characterize differences between input gradient attributions of standard and robust models.