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07/10/2023
Substance or Style: What Does Your Image Embedding Know?
Probes are small networks that predict properties of underlying data fro...
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06/01/2023
StyleDrop: Text-to-Image Generation in Any Style
Pre-trained large text-to-image models synthesize impressive images with...
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06/01/2023
StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learners
We investigate the potential of learning visual representations using sy...
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05/31/2023
Improving CLIP Training with Language Rewrites
Contrastive Language-Image Pre-training (CLIP) stands as one of the most...
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02/22/2023
Steerable Equivariant Representation Learning
Pre-trained deep image representations are useful for post-training task...
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11/16/2022
MAGE: MAsked Generative Encoder to Unify Representation Learning and Image Synthesis
Generative modeling and representation learning are two key tasks in com...
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10/30/2022
A simple, efficient and scalable contrastive masked autoencoder for learning visual representations
We introduce CAN, a simple, efficient and scalable method for self-super...
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12/01/2021
Object-Aware Cropping for Self-Supervised Learning
A core component of the recent success of self-supervised learning is cr...
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11/30/2021
Pyramid Adversarial Training Improves ViT Performance
Aggressive data augmentation is a key component of the strong generaliza...
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11/18/2021
Contrastive Multiview Coding for Enzyme-Substrate Interaction Prediction
Characterizing Enzyme function is an important requirement for predictin...
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08/14/2021
Unsupervised Disentanglement without Autoencoding: Pitfalls and Future Directions
Disentangled visual representations have largely been studied with gener...
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03/15/2021
Understanding invariance via feedforward inversion of discriminatively trained classifiers
A discriminatively trained neural net classifier achieves optimal perfor...
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05/20/2020
What makes for good views for contrastive learning
Contrastive learning between multiple views of the data has recently ach...
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03/25/2020
Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?
The focus of recent meta-learning research has been on the development o...
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12/04/2019
Fantastic Generalization Measures and Where to Find Them
Generalization of deep networks has been of great interest in recent yea...
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10/23/2019
Contrastive Representation Distillation
Often we wish to transfer representational knowledge from one neural net...
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08/19/2019
Boundless: Generative Adversarial Networks for Image Extension
Image extension models have broad applications in image editing, computa...
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07/04/2019
Adversarial Robustness through Local Linearization
Adversarial training is an effective methodology for training deep neura...
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06/13/2019
Contrastive Multiview Coding
Humans view the world through many sensory channels, e.g., the long-wave...
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06/10/2019
A Closed-Form Learned Pooling for Deep Classification Networks
In modern computer vision tasks, convolutional neural networks (CNNs) ar...
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09/28/2018
Predicting the Generalization Gap in Deep Networks with Margin Distributions
As shown in recent research, deep neural networks can perfectly fit rand...
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03/15/2018
Large Margin Deep Networks for Classification
We present a formulation of deep learning that aims at producing a large...
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12/21/2017
Smart, Sparse Contours to Represent and Edit Images
We study the problem of reconstructing an image from information stored ...
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01/17/2017
Synthesizing Normalized Faces from Facial Identity Features
We present a method for synthesizing a frontal, neutral-expression image...
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12/16/2016
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks
Collecting well-annotated image datasets to train modern machine learnin...
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08/22/2016
Domain Separation Networks
The cost of large scale data collection and annotation often makes the a...
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11/16/2013
