32 citations · 73 across the 12 of their papers we have counts for
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cs.CV2021
Spatially Consistent Representation Learning
Byungseok Roh, Wuhyun Shin, Ildoo Kim +1
Self-supervised learning has been widely used to obtain transferrable representations from unlabeled images. Especially, recent contrastive learning methods have shown impressive p…
cs.CV2020
Learning Loss for Test-Time Augmentation
Ildoo Kim, Younghoon Kim, Sungwoong Kim
Data augmentation has been actively studied for robust neural networks. Most of the recent data augmentation methods focus on augmenting datasets during the training phase. At the…
cs.CV2020
Spatially Attentive Output Layer for Image Classification
Ildoo Kim, Woonhyuk Baek, Sungwoong Kim
Most convolutional neural networks (CNNs) for image classification use a global average pooling (GAP) followed by a fully-connected (FC) layer for output logits. However, this spat…