84 citations · 197 across the 5 of their papers we have counts for
5 papers
CXR-CLIP: Toward Large Scale Chest X-ray Language-Image Pre-training
Kihyun You, Jawook Gu, Jiyeon Ham +5
A large-scale image-text pair dataset has greatly contributed to the development of vision-language pre-training (VLP) models, which enable zero-shot or few-shot classification wit…
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…
Scalable Neural Architecture Search for 3D Medical Image Segmentation
Sungwoong Kim, Ildoo Kim, Sungbin Lim +5
In this paper, a neural architecture search (NAS) framework is proposed for 3D medical image segmentation, to automatically optimize a neural architecture from a large design space…