32 citations · 96 across the 15 of their papers we have counts for
5 papers · 1 filter
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…
Visual Concept Reasoning Networks
Taesup Kim, Sungwoong Kim, Yoshua Bengio
A split-transform-merge strategy has been broadly used as an architectural constraint in convolutional neural networks for visual recognition tasks. It approximates sparsely connec…
AutoCLINT: The Winning Method in AutoCV Challenge 2019
Woonhyuk Baek, Ildoo Kim, Sungwoong Kim +1
NeurIPS 2019 AutoDL challenge is a series of six automated machine learning competitions. Particularly, AutoCV challenges mainly focused on classification tasks on visual domain. I…
torchgpipe: On-the-fly Pipeline Parallelism for Training Giant Models
Chiheon Kim, Heungsub Lee, Myungryong Jeong +5
We design and implement a ready-to-use library in PyTorch for performing micro-batch pipeline parallelism with checkpointing proposed by GPipe (Huang et al., 2019). In particular,…
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…