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cs.LG2021
Joining datasets via data augmentation in the label space for neural networks
Jake Zhao, Mingfeng Ou, Linji Xue +3
Most, if not all, modern deep learning systems restrict themselves to a single dataset for neural network training and inference. In this article, we are interested in systematic w…
cs.LG2021★ 43 cited
A critical look at the current train/test split in machine learning
Jimin Tan, Jianan Yang, Sai Wu +2
The randomized or cross-validated split of training and testing sets has been adopted as the gold standard of machine learning for decades. The establishment of these split protoco…
cs.LG2018★ 10 cited
Retrieval-Augmented Convolutional Neural Networks for Improved Robustness against Adversarial Examples
Jake Zhao, Kyunghyun Cho
We propose a retrieval-augmented convolutional network and propose to train it with local mixup, a novel variant of the recently proposed mixup algorithm. The proposed hybrid archi…