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A Simple Randomization Technique for Generalization in Deep Reinforcement Learning
Deep reinforcement learning (RL) agents often fail to generalize to unse...
10/11/2019 ∙ by Kimin Lee, et al. ∙
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Using Pre-Training Can Improve Model Robustness and Uncertainty
Tuning a pre-trained network is commonly thought to improve data efficie...
01/28/2019 ∙ by Dan Hendrycks, et al. ∙
16
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Incremental Learning with Unlabeled Data in the Wild
Deep neural networks are known to suffer from catastrophic forgetting in...
03/29/2019 ∙ by Kibok Lee, et al. ∙
12
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Robust Inference via Generative Classifiers for Handling Noisy Labels
Large-scale datasets may contain significant proportions of noisy (incor...
01/31/2019 ∙ by Kimin Lee, et al. ∙
6
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A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
Detecting test samples drawn sufficiently far away from the training dis...
07/10/2018 ∙ by Kimin Lee, et al. ∙
2
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Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples
The problem of detecting whether a test sample is from in-distribution (...
11/26/2017 ∙ by Kimin Lee, et al. ∙
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Confident Multiple Choice Learning
Ensemble methods are arguably the most trustworthy techniques for boosti...
06/12/2017 ∙ by Kimin Lee, et al. ∙
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Hierarchical Novelty Detection for Visual Object Recognition
Deep neural networks have achieved impressive success in large-scale vis...
04/02/2018 ∙ by Kibok Lee, et al. ∙
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