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Found 16 papers, 15 papers with code
Sparse autoencoders reveal selective remapping of visual concepts during adaptation
Adapting foundation models for specific purposes has become a standard approach to build machine learning systems for downstream applications.
Self-supervised contrastive learning performs non-linear system identification
Self-supervised learning (SSL) approaches have brought tremendous success across many tasks and domains.
RDumb: A simple approach that questions our progress in continual test-time adaptation
Test-Time Adaptation (TTA) allows to update pre-trained models to changing data distributions at deployment time.
Multi-animal pose estimation, identification and tracking with DeepLabCut
Estimating the pose of multiple animals is a challenging computer vision problem: frequent interactions cause occlusions and complicate the association of detected keypoints to the correct individuals, as well as having highly similar looking animals that interact more closely than in typical multi-human scenarios.
Ranked #4 on
Animal Pose Estimation
on TriMouse-161
Learnable latent embeddings for joint behavioral and neural analysis
Mapping behavioral actions to neural activity is a fundamental goal of neuroscience.
SuperAnimal pretrained pose estimation models for behavioral analysis
We illustrate the utility of our models in behavioral classification in mice and gait analysis in horses.
Ranked #1 on
Animal Pose Estimation
on Animal-Pose Dataset
(using extra training data)
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-A
(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.
