Search Results for author:
Found 11 papers, 9 papers with code
Learnable latent embeddings for joint behavioral and neural analysis
Mapping behavioral actions to neural activity is a fundamental goal of neuroscience.
Unsupervised Object Learning via Common Fate
Learning generative object models from unlabelled videos is a long standing problem and required for causal scene modeling.
If your data distribution shifts, use self-learning
We demonstrate that self-learning techniques like entropy minimization and pseudo-labeling are simple and effective at improving performance of a deployed computer vision model under systematic domain shifts.
Ranked #1 on
Unsupervised Domain Adaptation
on ImageNet-C
(using extra training data)
Out-of-distribution generalization of internal models is correlated with reward
We investigate the behavior of reinforcement learning (RL) agents under morphological distribution shifts.
Out-of-Distribution Generalization
reinforcement-learning
+1
Contrastive Learning Inverts the Data Generating Process
Contrastive learning has recently seen tremendous success in self-supervised learning.
Ranked #1 on
Disentanglement
on KITTI-Masks
A Primer on Motion Capture with Deep Learning: Principles, Pitfalls and Perspectives
Extracting behavioral measurements non-invasively from video is stymied by the fact that it is a hard computational problem.
Improving robustness against common corruptions by covariate shift adaptation
With the more robust DeepAugment+AugMix model, we improve the state of the art achieved by a ResNet50 model up to date from 53. 6% mCE to 45. 4% mCE.
Ranked #4 on
Unsupervised Domain Adaptation
on ImageNet-R
vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations
We propose vq-wav2vec to learn discrete representations of audio segments through a wav2vec-style self-supervised context prediction task.
Ranked #2 on
Speech Recognition
on TIMIT
(using extra training data)
Pretraining boosts out-of-domain robustness for pose estimation
Neural networks are highly effective tools for pose estimation.
Ranked #1 on
Animal Pose Estimation
on Horse-10
wav2vec: Unsupervised Pre-training for Speech Recognition
Our experiments on WSJ reduce WER of a strong character-based log-mel filterbank baseline by up to 36% when only a few hours of transcribed data is available.
Ranked #5 on
Speech Recognition
on TIMIT
(using extra training data)
Context-based Normalization of Histological Stains using Deep Convolutional Features
While human observers are able to cope with variations in color and appearance of histological stains, digital pathology algorithms commonly require a well-normalized setting to achieve peak performance, especially when a limited amount of labeled data is available.
