377 citations · 402 across the 5 of their papers we have counts for
5 papers
Deep Anomaly Detection by Residual Adaptation
Lucas Deecke, Lukas Ruff, Robert A. Vandermeulen +1
Deep anomaly detection is a difficult task since, in high dimensions, it is hard to completely characterize a notion of "differentness" when given only examples of normality. In th…
Knowledge Distillation for Multi-task Learning
Wei-Hong Li, Hakan Bilen
Multi-task learning (MTL) is to learn one single model that performs multiple tasks for achieving good performance on all tasks and lower cost on computation. Learning such a model…
iDLG: Improved Deep Leakage from Gradients
Bo Zhao, Konda Reddy Mopuri, Hakan Bilen
It is widely believed that sharing gradients will not leak private training data in distributed learning systems such as Collaborative Learning and Federated Learning, etc. Recentl…
NormGrad: Finding the Pixels that Matter for Training
Sylvestre-Alvise Rebuffi, Ruth Fong, Xu Ji +2
The different families of saliency methods, either based on contrastive signals, closed-form formulas mixing gradients with activations or on perturbation masks, all focus on which…
ResearchDoom and CocoDoom: Learning Computer Vision with Games
A. Mahendran, H. Bilen, J. F. Henriques +1
In this short note we introduce ResearchDoom, an implementation of the Doom first-person shooter that can extract detailed metadata from the game. We also introduce the CocoDoom da…