Emergence of Invariance and Disentanglement in Deep Representations

5 June 2017

Papers citing "Emergence of Invariance and Disentanglement in Deep Representations"

50 / 112 papers shown

Title
Single-Training Collaborative Object Detectors Adaptive to Bandwidth and Computation Juliano S. Assine José Cândido Silveira Santos Filho Eduardo Valle ObjD 50 8 0 03 May 2021
Rapid Exploration for Open-World Navigation with Latent Goal Models Dhruv Shah Benjamin Eysenbach G. Kahn Nicholas Rhinehart Sergey Levine 32 70 0 12 Apr 2021
Learning Invariant Representation of Tasks for Robust Surgical State Estimation Yidan Qin M. Allan Yisong Yue J. W. Burdick M. Azizian OOD 11 8 0 18 Feb 2021
A Variational Information Bottleneck Approach to Multi-Omics Data Integration Changhee Lee M. Schaar DRL 20 74 0 05 Feb 2021
Estimating informativeness of samples with Smooth Unique Information Hrayr Harutyunyan Alessandro Achille Giovanni Paolini Orchid Majumder Avinash Ravichandran Rahul Bhotika Stefano Soatto 27 24 0 17 Jan 2021
Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting Zeke Xie Fengxiang He Shaopeng Fu Issei Sato Dacheng Tao Masashi Sugiyama 21 60 0 12 Nov 2020
Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness Long Zhao Ting Liu Xi Peng Dimitris N. Metaxas OOD AAML 27 165 0 15 Oct 2020
Respecting Domain Relations: Hypothesis Invariance for Domain Generalization Ziqi Wang Marco Loog Jan van Gemert OOD 24 48 0 15 Oct 2020
Usable Information and Evolution of Optimal Representations During Training Michael Kleinman Alessandro Achille Daksh Idnani J. Kao 29 13 0 06 Oct 2020
Learning Optimal Representations with the Decodable Information Bottleneck Yann Dubois Douwe Kiela D. Schwab Ramakrishna Vedantam 26 43 0 27 Sep 2020
Domain-invariant Similarity Activation Map Contrastive Learning for Retrieval-based Long-term Visual Localization Hanjiang Hu Hesheng Wang Zhe Liu Weidong Chen 32 27 0 16 Sep 2020
DualDE: Dually Distilling Knowledge Graph Embedding for Faster and Cheaper Reasoning Yushan Zhu Wen Zhang Mingyang Chen Hui Chen Xu-Xin Cheng Wei Zhang Huajun Chen Zhejiang University 22 3 0 13 Sep 2020
Measuring the Biases and Effectiveness of Content-Style Disentanglement Xiao Liu Spyridon Thermos Gabriele Valvano A. Chartsias Alison Q. OÑeil Sotirios A. Tsaftaris CoGe DRL 32 18 0 27 Aug 2020
When is invariance useful in an Out-of-Distribution Generalization problem ? Masanori Koyama Shoichiro Yamaguchi OOD 34 65 0 04 Aug 2020
$Beyond $\mathcal{H}$-Divergence: Domain Adaptation Theory With Jensen-Shannon Divergence$ Beyond $\mathcal{H}$ -Divergence: Domain Adaptation Theory With Jensen-Shannon Divergence Changjian Shui Qi Chen Jun Wen Fan Zhou Christian Gagné Boyu Wang 38 22 0 30 Jul 2020
Tighter risk certificates for neural networks Maria Perez-Ortiz Omar Rivasplata John Shawe-Taylor Csaba Szepesvári UQCV 20 102 0 25 Jul 2020
Adversarial Training Reduces Information and Improves Transferability M. Terzi Alessandro Achille Marco Maggipinto Gian Antonio Susto AAML 24 23 0 22 Jul 2020
Interpreting and Disentangling Feature Components of Various Complexity from DNNs Jie Ren Mingjie Li Zexu Liu Quanshi Zhang CoGe 19 18 0 29 Jun 2020
A Variational Approach to Privacy and Fairness Borja Rodríguez Gálvez Ragnar Thobaben Mikael Skoglund FaML DRL 19 25 0 11 Jun 2020
On the Maximum Mutual Information Capacity of Neural Architectures Brandon Foggo Nan Yu TPM 23 3 0 10 Jun 2020
Generalization Bounds via Information Density and Conditional Information Density Fredrik Hellström G. Durisi 27 65 0 16 May 2020
Learning to Manipulate Individual Objects in an Image Yanchao Yang Yutong Chen Stefano Soatto OCL 13 37 0 11 Apr 2020
Adversarial Latent Autoencoders Stanislav Pidhorskyi Donald Adjeroh Gianfranco Doretto GAN DRL 51 259 0 09 Apr 2020
Guided Variational Autoencoder for Disentanglement Learning Zheng Ding Yifan Xu Weijian Xu Gaurav Parmar Yang Yang Max Welling Zhuowen Tu DRL CoGe 34 106 0 02 Apr 2020
Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning Andreas Kirsch Clare Lyle Y. Gal 27 16 0 27 Mar 2020
On Information Plane Analyses of Neural Network Classifiers -- A Review Bernhard C. Geiger 32 50 0 21 Mar 2020
The Variational InfoMax Learning Objective Vincenzo Crescimanna Bruce P. Graham 21 0 0 07 Mar 2020
Semi-Supervised StyleGAN for Disentanglement Learning Weili Nie Tero Karras Animesh Garg Shoubhik Debhath Anjul Patney Ankit B. Patel Anima Anandkumar DRL 89 72 0 06 Mar 2020
Forgetting Outside the Box: Scrubbing Deep Networks of Information Accessible from Input-Output Observations Aditya Golatkar Alessandro Achille Stefano Soatto MU OOD 22 189 0 05 Mar 2020
NestedVAE: Isolating Common Factors via Weak Supervision M. Vowels Necati Cihan Camgöz Richard Bowden CML DRL 26 21 0 26 Feb 2020
Convolutional Tensor-Train LSTM for Spatio-temporal Learning Jiahao Su Wonmin Byeon Jean Kossaifi Furong Huang Jan Kautz Anima Anandkumar AI4TS 19 119 0 21 Feb 2020
Bayesian Deep Learning and a Probabilistic Perspective of Generalization A. Wilson Pavel Izmailov UQCV BDL OOD 24 641 0 20 Feb 2020
CEB Improves Model Robustness Ian S. Fischer Alexander A. Alemi AAML 19 28 0 13 Feb 2020
Unsupervised Representation Disentanglement using Cross Domain Features and Adversarial Learning in Variational Autoencoder based Voice Conversion Wen-Chin Huang Hao Luo Hsin-Te Hwang Chen-Chou Lo Yu-Huai Peng Yu Tsao Hsin-Min Wang DRL 17 42 0 22 Jan 2020
Learning credit assignment Chan Li Haiping Huang 20 7 0 10 Jan 2020
Information in Infinite Ensembles of Infinitely-Wide Neural Networks Ravid Shwartz-Ziv Alexander A. Alemi 24 21 0 20 Nov 2019
Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Jan Stühmer Richard Turner Sebastian Nowozin DRL BDL CoGe 117 25 0 05 Sep 2019
Tuning-Free Disentanglement via Projection Yue Bai L. Duan 21 3 0 27 Jun 2019
The Variational InfoMax AutoEncoder Vincenzo Crescimanna Bruce P. Graham DRL 12 3 0 25 May 2019
Hangul Fonts Dataset: a Hierarchical and Compositional Dataset for Investigating Learned Representations J. Livezey Ahyeon Hwang Jacob Yeung K. Bouchard 36 0 0 23 May 2019
Minimal Achievable Sufficient Statistic Learning Milan Cvitkovic Günther Koliander 25 12 0 19 May 2019
The Information Complexity of Learning Tasks, their Structure and their Distance Alessandro Achille Giovanni Paolini G. Mbeng Stefano Soatto 11 51 0 05 Apr 2019
Task2Vec: Task Embedding for Meta-Learning Alessandro Achille Michael Lam Rahul Tewari Avinash Ravichandran Subhransu Maji Charless C. Fowlkes Stefano Soatto Pietro Perona SSL 28 309 0 10 Feb 2019
Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology Bastian Alexander Rieck Matteo Togninalli Christian Bock Michael Moor Max Horn Thomas Gumbsch Karsten M. Borgwardt 36 111 0 23 Dec 2018
Statistical Characteristics of Deep Representations: An Empirical Investigation Daeyoung Choi Kyungeun Lee Changho Shin Stephen J. Roberts AI4TS 18 2 0 08 Nov 2018
Excessive Invariance Causes Adversarial Vulnerability J. Jacobsen Jens Behrmann R. Zemel Matthias Bethge AAML 33 166 0 01 Nov 2018
Analyzing biological and artificial neural networks: challenges with opportunities for synergy? David Barrett Ari S. Morcos Jakob H. Macke AI4CE 25 110 0 31 Oct 2018
Interpretable Neuron Structuring with Graph Spectral Regularization Alexander Tong David van Dijk Jay S. Stanley Matthew Amodio Kristina M. Yim R. Muhle J. Noonan Guy Wolf Smita Krishnaswamy 32 6 0 30 Sep 2018
Distributed Variational Representation Learning Iñaki Estella Aguerri Milad Sefidgaran 23 71 0 11 Jul 2018
Deformable Generator Networks: Unsupervised Disentanglement of Appearance and Geometry X. Xing Ruiqi Gao Tian Han Song-Chun Zhu Ying Nian Wu DRL 24 28 0 16 Jun 2018