21 citations · 27 across the 4 of their papers we have counts for
4 papers
Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision
Denis Gudovskiy, Alec Hodgkinson, Takuya Yamaguchi +1
Active learning (AL) aims to minimize labeling efforts for data-demanding deep neural networks (DNNs) by selecting the most representative data points for annotation. However, curr…
DoorGym: A Scalable Door Opening Environment And Baseline Agent
Yusuke Urakami, Alec Hodgkinson, Casey Carlin +3
In order to practically implement the door opening task, a policy ought to be robust to a wide distribution of door types and environment settings. Reinforcement Learning (RL) with…
Explain to Fix: A Framework to Interpret and Correct DNN Object Detector Predictions
Denis Gudovskiy, Alec Hodgkinson, Takuya Yamaguchi +2
Explaining predictions of deep neural networks (DNNs) is an important and nontrivial task. In this paper, we propose a practical approach to interpret decisions made by a DNN objec…
DNN Feature Map Compression using Learned Representation over GF(2)
Denis A. Gudovskiy, Alec Hodgkinson, Luca Rigazio
In this paper, we introduce a method to compress intermediate feature maps of deep neural networks (DNNs) to decrease memory storage and bandwidth requirements during inference. Un…