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04/14/2023
M2T: Masking Transformers Twice for Faster Decoding
We show how bidirectional transformers trained for masked token predicti...
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12/15/2022
Image-and-Language Understanding from Pixels Only
Multimodal models are becoming increasingly effective, in part due to un...
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12/15/2022
FlexiViT: One Model for All Patch Sizes
Vision Transformers convert images to sequences by slicing them into pat...
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03/29/2022
Neural Face Video Compression using Multiple Views
Recent advances in deep generative models led to the development of neur...
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10/06/2020
Representation learning from videos in-the-wild: An object-centric approach
We propose a method to learn image representations from uncurated videos...
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07/16/2020
On Robustness and Transferability of Convolutional Neural Networks
Modern deep convolutional networks (CNNs) are often criticized for not g...
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06/17/2020
High-Fidelity Generative Image Compression
We extensively study how to combine Generative Adversarial Networks and ...
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03/23/2020
Learning Better Lossless Compression Using Lossy Compression
We leverage the powerful lossy image compression algorithm BPG to build ...
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02/20/2020
Automatic Shortcut Removal for Self-Supervised Representation Learning
In self-supervised visual representation learning, a feature extractor i...
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02/07/2020
Weakly-Supervised Disentanglement Without Compromises
Intelligent agents should be able to learn useful representations by obs...
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12/05/2019
Self-Supervised Learning of Video-Induced Visual Invariances
We propose a general framework for self-supervised learning of transfera...
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11/26/2019
Semantic Bottleneck Scene Generation
Coupling the high-fidelity generation capabilities of label-conditional ...
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07/31/2019
On Mutual Information Maximization for Representation Learning
Many recent methods for unsupervised or self-supervised representation l...
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05/03/2019
Disentangling Factors of Variation Using Few Labels
Learning disentangled representations is considered a cornerstone proble...
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03/06/2019
High-Fidelity Image Generation With Fewer Labels
Deep generative models are becoming a cornerstone of modern machine lear...
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12/12/2018
Recent Advances in Autoencoder-Based Representation Learning
Learning useful representations with little or no supervision is a key c...
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11/30/2018
Practical Full Resolution Learned Lossless Image Compression
We propose the first practical learned lossless image compression system...
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05/28/2018
Deep Generative Models for Distribution-Preserving Lossy Compression
We propose and study the problem of distribution-preserving lossy compre...
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05/12/2018
Born Again Neural Networks
Knowledge distillation (KD) consists of transferring knowledge from one ...
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04/09/2018
Generative Adversarial Networks for Extreme Learned Image Compression
We propose a framework for extreme learned image compression based on Ge...
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03/16/2018
Towards Image Understanding from Deep Compression without Decoding
Motivated by recent work on deep neural network (DNN)-based image compre...
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01/12/2018
Conditional Probability Models for Deep Image Compression
Deep Neural Networks trained as image auto-encoders have recently emerge...
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12/11/2017
StrassenNets: Deep learning with a multiplication budget
A large fraction of the arithmetic operations required to evaluate deep ...
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05/31/2017
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees
Greedy optimization methods such as Matching Pursuit (MP) and Frank-Wolf...
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04/03/2017
Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations
We present a new approach to learn compressible representations in deep ...
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02/21/2017
A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe
Two of the most fundamental prototypes of greedy optimization are the ma...
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09/26/2016
Deep Structured Features for Semantic Segmentation
We propose a highly structured neural network architecture for semantic ...
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05/26/2016
Discrete Deep Feature Extraction: A Theory and New Architectures
First steps towards a mathematical theory of deep convolutional neural n...
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02/12/2016
Pursuits in Structured Non-Convex Matrix Factorizations
Efficiently representing real world data in a succinct and parsimonious ...
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04/20/2015
Nonparametric Nearest Neighbor Random Process Clustering
We consider the problem of clustering noisy finite-length observations o...
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04/27/2014