{"list":"other","total":58,"rows":[{"id":"1409.0575","title":"ImageNet Large Scale Visual Recognition Challenge","created":"2014-09-01","cited_by_count":8498},{"id":"1610.02391","title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization","created":"2016-10-07","cited_by_count":6733},{"id":"1705.07874","title":"A Unified Approach to Interpreting Model Predictions","created":"2017-05-22","cited_by_count":6555},{"id":"1602.04938","title":"\"Why Should I Trust You?\": Explaining the Predictions of Any Classifier","created":"2016-02-16","cited_by_count":5070},{"id":"1703.01365","title":"Axiomatic Attribution for Deep Networks","created":"2017-03-04","cited_by_count":3384},{"id":"1512.04150","title":"Learning Deep Features for Discriminative Localization","created":"2015-12-14","cited_by_count":2436},{"id":"1312.6034","title":"Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps","created":"2013-12-20","cited_by_count":2371},{"id":"1311.2901","title":"Visualizing and Understanding Convolutional Networks","created":"2013-11-12","cited_by_count":2290},{"id":"1308.3432","title":"Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation","created":"2013-08-15","cited_by_count":2079},{"id":"1706.03825","title":"SmoothGrad: removing noise by adding noise","created":"2017-06-12","cited_by_count":1109},{"id":"1802.01933","title":"A Survey Of Methods For Explaining Black Box Models","created":"2018-02-06","cited_by_count":973},{"id":"1811.12359","title":"Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations","created":"2018-11-29","cited_by_count":941},{"id":"1902.06720","title":"Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent","created":"2019-02-18","cited_by_count":738},{"id":"1806.10574","title":"This Looks Like That: Deep Learning for Interpretable Image Recognition","created":"2018-06-27","cited_by_count":737},{"id":"1703.10593","title":"Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks","created":"2017-03-30","cited_by_count":707},{"id":"1704.03296","title":"Interpretable Explanations of Black Boxes by Meaningful Perturbation","created":"2017-04-11","cited_by_count":612},{"id":"1606.06160","title":"DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients","created":"2016-06-20","cited_by_count":569},{"id":"1707.00600","title":"Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly","created":"2017-07-03","cited_by_count":538},{"id":"1806.07538","title":"Towards Robust Interpretability with Self-Explaining Neural Networks","created":"2018-06-20","cited_by_count":505},{"id":"1609.07061","title":"Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations","created":"2016-09-22","cited_by_count":426},{"id":"1608.00507","title":"Top-down Neural Attention by Excitation Backprop","created":"2016-08-01","cited_by_count":321},{"id":"1806.08049","title":"On the Robustness of Interpretability Methods","created":"2018-06-21","cited_by_count":314},{"id":"1605.01713","title":"Not Just a Black Box: Learning Important Features Through Propagating Activation Differences","created":"2016-05-05","cited_by_count":297},{"id":"1703.05192","title":"Learning to Discover Cross-Domain Relations with Generative Adversarial Networks","created":"2017-03-15","cited_by_count":278},{"id":"1910.08485","title":"Understanding Deep Networks via Extremal Perturbations and Smooth Masks","created":"2019-10-18","cited_by_count":259},{"id":"1512.02479","title":"Explaining NonLinear Classification Decisions with Deep Taylor Decomposition","created":"2015-12-08","cited_by_count":250},{"id":"1705.07857","title":"Real Time Image Saliency for Black Box Classifiers","created":"2017-05-22","cited_by_count":246},{"id":"1711.09784","title":"Distilling a Neural Network Into a Soft Decision Tree","created":"2017-11-27","cited_by_count":236},{"id":"1905.00780","title":"Full-Gradient Representation for Neural Network Visualization","created":"2019-05-02","cited_by_count":191},{"id":"1802.10171","title":"Tell Me Where to Look: Guided Attention Inference Network","created":"2018-02-27","cited_by_count":169},{"id":"1911.09464","title":"Quantization Networks","created":"2019-11-21","cited_by_count":153},{"id":"1611.00179","title":"Dual Learning for Machine Translation","created":"2016-11-01","cited_by_count":148},{"id":"1812.05069","title":"Recent Advances in Autoencoder-Based Representation Learning","created":"2018-12-12","cited_by_count":133},{"id":"1711.05611","title":"Interpreting Deep Visual Representations via Network Dissection","created":"2017-11-15","cited_by_count":103},{"id":"1809.00287","title":"Learning to Navigate for Fine-grained Classification","created":"2018-09-02","cited_by_count":103},{"id":"1512.02017","title":"Visualizing Deep Convolutional Neural Networks Using Natural Pre-Images","created":"2015-12-07","cited_by_count":93},{"id":"2003.03836","title":"Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches","created":"2020-03-08","cited_by_count":92},{"id":"2002.10191","title":"Learning Attentive Pairwise Interaction for Fine-Grained Classification","created":"2020-02-24","cited_by_count":84},{"id":"1904.13132","title":"A critical analysis of self-supervision, or what we can learn from a single image","created":"2019-04-30","cited_by_count":76},{"id":"2005.10411","title":"Interpretable and Accurate Fine-grained Recognition via Region Grouping","created":"2020-05-21","cited_by_count":75},{"id":"1807.06699","title":"Adaptive Neural Trees","created":"2018-07-17","cited_by_count":64},{"id":"1901.09891","title":"See Better Before Looking Closer: Weakly Supervised Data Augmentation Network for Fine-Grained Visual Classification","created":"2019-01-26","cited_by_count":56},{"id":"1904.00605","title":"Relative Attributing Propagation: Interpreting the Comparative Contributions of Individual Units in Deep Neural Networks","created":"2019-04-01","cited_by_count":49},{"id":"1812.02100","title":"Understanding Individual Decisions of CNNs via Contrastive Backpropagation","created":"2018-12-05","cited_by_count":40},{"id":"1710.05050","title":"Learning Independent Features with Adversarial Nets for Non-linear ICA","created":"2017-10-13","cited_by_count":33},{"id":"1910.06956","title":"Neural tangent kernels, transportation mappings, and universal approximation","created":"2019-10-15","cited_by_count":30},{"id":"1908.04351","title":"Explaining Convolutional Neural Networks using Softmax Gradient Layer-wise Relevance Propagation","created":"2019-08-06","cited_by_count":29},{"id":"1311.5591","title":"PANDA: Pose Aligned Networks for Deep Attribute Modeling","created":"2013-11-21","cited_by_count":23},{"id":"2012.02166","title":"Visualization of Supervised and Self-Supervised Neural Networks via Attribution Guided Factorization","created":"2020-12-03","cited_by_count":23},{"id":"2006.05553","title":"Neural Methods for Point-wise Dependency Estimation","created":"2020-06-09","cited_by_count":22},{"id":"1805.10583","title":"Dual Swap Disentangling","created":"2018-05-27","cited_by_count":12},{"id":"1906.08652","title":"Disentangling Influence: Using Disentangled Representations to Audit Model Predictions","created":"2019-06-20","cited_by_count":11},{"id":"1805.08838","title":"Clustering - What Both Theoreticians and Practitioners are Doing Wrong","created":"2018-05-22","cited_by_count":8},{"id":"2101.08527","title":"Progressive Co-Attention Network for Fine-grained Visual Classification","created":"2021-01-21","cited_by_count":5},{"id":"1709.00074","title":"The Role of Minimal Complexity Functions in Unsupervised Learning of Semantic Mappings","created":"2017-08-31","cited_by_count":4},{"id":"1912.09140","title":"Meta Decision Trees for Explainable Recommendation Systems","created":"2019-12-19","cited_by_count":4},{"id":"1905.03658","title":"Improving Discrete Latent Representations With Differentiable Approximation Bridges","created":"2019-05-09","cited_by_count":2},{"id":"1710.09047","title":"Block Coordinate Descent Only Converge to Minimizers","created":"2017-10-25","cited_by_count":1}],"next_cursor":null}