60 citations · 114 across the 3 of their papers we have counts for
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stat.ML2019★ 60 cited
Robust Inference via Generative Classifiers for Handling Noisy Labels
Kimin Lee, Sukmin Yun, Kibok Lee +3
Large-scale datasets may contain significant proportions of noisy (incorrect) class labels, and it is well-known that modern deep neural networks (DNNs) poorly generalize from such…
stat.ML2018
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
Kimin Lee, Kibok Lee, Honglak Lee +1
Detecting test samples drawn sufficiently far away from the training distribution statistically or adversarially is a fundamental requirement for deploying a good classifier in man…
stat.ML2017
Towards Understanding the Invertibility of Convolutional Neural Networks
Anna C. Gilbert, Yi Zhang, Kibok Lee +2
Several recent works have empirically observed that Convolutional Neural Nets (CNNs) are (approximately) invertible. To understand this approximate invertibility phenomenon and how…