289 citations · 554 across the 28 of their papers we have counts for
6 papers · 1 filter
A Maximal Correlation Approach to Imposing Fairness in Machine Learning
Joshua Lee, Yuheng Bu, Prasanna Sattigeri +4
As machine learning algorithms grow in popularity and diversify to many industries, ethical and legal concerns regarding their fairness have become increasingly relevant. We explor…
Fine-grained Angular Contrastive Learning with Coarse Labels
Guy Bukchin, Eli Schwartz, Kate Saenko +4
Few-shot learning methods offer pre-training techniques optimized for easier later adaptation of the model to new classes (unseen during training) using one or a few examples. This…
OnlineAugment: Online Data Augmentation with Less Domain Knowledge
Zhiqiang Tang, Yunhe Gao, Leonid Karlinsky +3
Data augmentation is one of the most important tools in training modern deep neural networks. Recently, great advances have been made in searching for optimal augmentation policies…
AR-Net: Adaptive Frame Resolution for Efficient Action Recognition
Yue Meng, Chung-Ching Lin, Rameswar Panda +5
Action recognition is an open and challenging problem in computer vision. While current state-of-the-art models offer excellent recognition results, their computational expense lim…
TAFSSL: Task-Adaptive Feature Sub-Space Learning for few-shot classification
Moshe Lichtenstein, Prasanna Sattigeri, Rogerio Feris +2
The field of Few-Shot Learning (FSL), or learning from very few (typically or ) examples per novel class (unseen during training), has received a lot of attention and signif…
StarNet: towards Weakly Supervised Few-Shot Object Detection
Leonid Karlinsky, Joseph Shtok, Amit Alfassy +8
Few-shot detection and classification have advanced significantly in recent years. Yet, detection approaches require strong annotation (bounding boxes) both for pre-training and fo…