3 citations · 6 across the 4 of their papers we have counts for
3 papers · 1 filter
AutoDO: Robust AutoAugment for Biased Data with Label Noise via Scalable Probabilistic Implicit Differentiation
Denis Gudovskiy, Luca Rigazio, Shun Ishizaka +2
AutoAugment has sparked an interest in automated augmentation methods for deep learning models. These methods estimate image transformation policies for train data that improve gen…
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…
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…