1 code implementation • CVPR 2020 • Denis Gudovskiy, Alec Hodgkinson, Takuya Yamaguchi, Sotaro Tsukizawa
We theoretically derive an optimal acquisition function for AL in this setting.
1 code implementation • 5 Aug 2019 • Yusuke Urakami, Alec Hodgkinson, Casey Carlin, Randall Leu, Luca Rigazio, Pieter Abbeel
We introduce DoorGym, an open-source door opening simulation framework designed to utilize domain randomization to train a stable policy.
no code implementations • ICLR Workshop LLD 2019 • Denis Gudovskiy, Alec Hodgkinson, Takuya Yamaguchi, Sotaro Tsukizawa
We introduce an attention mechanism to improve feature extraction for deep active learning (AL) in the semi-supervised setting.
1 code implementation • 19 Nov 2018 • Denis Gudovskiy, Alec Hodgkinson, Takuya Yamaguchi, Yasunori Ishii, Sotaro Tsukizawa
We qualitatively and quantitatively show that the proposed explanation method can be used to find image features which cause failures in DNN object detection.
1 code implementation • ICLR 2018 • 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.