162 citations · 303 across the 20 of their papers we have counts for
6 papers · 1 filter
EDAS: Efficient and Differentiable Architecture Search
Hyeong Gwon Hong, Pyunghwan Ahn, Junmo Kim
Transferrable neural architecture search can be viewed as a binary optimization problem where a single optimal path should be selected among candidate paths in each edge within the…
Joint Learning of Generative Translator and Classifier for Visually Similar Classes
ByungIn Yoo, Tristan Sylvain, Yoshua Bengio +1
In this paper, we propose a Generative Translation Classification Network (GTCN) for improving visual classification accuracy in settings where classes are visually similar and dat…
Adjusting Decision Boundary for Class Imbalanced Learning
Byungju Kim, Junmo Kim
Training of deep neural networks heavily depends on the data distribution. In particular, the networks easily suffer from class imbalance. The trained networks would recognize the…
Cut-and-Paste Dataset Generation for Balancing Domain Gaps in Object Instance Detection
Woo-han Yun, Taewoo Kim, Jaeyeon Lee +2
Training an object instance detector where only a few training object images are available is a challenging task. One solution is a cut-and-paste method that generates a training d…
NLNL: Negative Learning for Noisy Labels
Youngdong Kim, Junho Yim, Juseung Yun +1
Convolutional Neural Networks (CNNs) provide excellent performance when used for image classification. The classical method of training CNNs is by labeling images in a supervised m…
RRNet: Repetition-Reduction Network for Energy Efficient Decoder of Depth Estimation
Sangyun Oh, Hye-Jin S. Kim, Jongeun Lee +1
We introduce Repetition-Reduction network (RRNet) for resource-constrained depth estimation, offering significantly improved efficiency in terms of computation, memory and energy c…