Search Results for author: Wen-Hsuan Chu

Found 2 papers, 0 papers with code

Neural Batch Sampling with Reinforcement Learning for Semi-Supervised Anomaly Detection

no code implementations ECCV 2020 Wen-Hsuan Chu, Kris M. Kitani

In this work, our key hypothesis is that this change in loss values during training can be used as a feature to identify anomalous data.

Anomaly Detection reinforcement-learning +1

Spot and Learn: A Maximum-Entropy Patch Sampler for Few-Shot Image Classification

no code implementations CVPR 2019 Wen-Hsuan Chu, Yu-Jhe Li, Jing-Cheng Chang, Yu-Chiang Frank Wang

Few-shot learning (FSL) requires one to learn from object categories with a small amount of training data (as novel classes), while the remaining categories (as base classes) contain a sufficient amount of data for training.

Data Augmentation Few-Shot Image Classification +1

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