289 citations · 1.3k across the 22 of their papers we have counts for
5 papers · 1 filter
Understanding Unequal Gender Classification Accuracy from Face Images
Vidya Muthukumar, Tejaswini Pedapati, Nalini Ratha +7
Recent work shows unequal performance of commercial face classification services in the gender classification task across intersectional groups defined by skin type and gender. Acc…
SpotTune: Transfer Learning through Adaptive Fine-tuning
Yunhui Guo, Honghui Shi, Abhishek Kumar +3
Transfer learning, which allows a source task to affect the inductive bias of the target task, is widely used in computer vision. The typical way of conducting transfer learning wi…
Delta-encoder: an effective sample synthesis method for few-shot object recognition
Eli Schwartz, Leonid Karlinsky, Joseph Shtok +6
Learning to classify new categories based on just one or a few examples is a long-standing challenge in modern computer vision. In this work, we proposes a simple yet effective met…
BlockDrop: Dynamic Inference Paths in Residual Networks
Zuxuan Wu, Tushar Nagarajan, Abhishek Kumar +4
Very deep convolutional neural networks offer excellent recognition results, yet their computational expense limits their impact for many real-world applications. We introduce Bloc…
Fully-adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification
Yongxi Lu, Abhishek Kumar, Shuangfei Zhai +3
Multi-task learning aims to improve generalization performance of multiple prediction tasks by appropriately sharing relevant information across them. In the context of deep neural…