The Details Matter: Preventing Class Collapse in Supervised Contrastive Learning

no code implementations • 29 Sep 2021 • Daniel Yang Fu, Mayee F Chen, Michael Zhang, Kayvon Fatahalian, Christopher Ré

Supervised contrastive learning optimizes a loss that pushes together embeddings of points from the same class while pulling apart embeddings of points from different classes.

Contrastive Learning Transfer Learning

Beyond the Pixels: Exploring the Effects of Bit-Level Network and File Corruptions on Video Model Robustness

no code implementations • 1 Jan 2021 • Trenton Chang, Daniel Yang Fu, Yixuan Li

We investigate the robustness of video machine learning models to bit-level network and file corruptions, which can arise from network transmission failures or hardware errors, and explore defenses against such corruptions.

Action Recognition Multi-Object Tracking