11 citations · 22 across the 6 of their papers we have counts for
9 papers · 1 filter
GrowTAS: Progressive Expansion from Small to Large Subnets for Efficient ViT Architecture Search
Hyunju Lee, Youngmin Oh, Jeimin Jeon +2
Transformer architecture search (TAS) aims to automatically discover efficient vision transformers (ViTs), reducing the need for manual design. Existing TAS methods typically train…
Subnet-Aware Dynamic Supernet Training for Neural Architecture Search
Jeimin Jeon, Youngmin Oh, Junghyup Lee +4
N-shot neural architecture search (NAS) exploits a supernet containing all candidate subnets for a given search space. The subnets are typically trained with a static training stra…
Efficient Few-Shot Neural Architecture Search by Counting the Number of Nonlinear Functions
Youngmin Oh, Hyunju Lee, Bumsub Ham
Neural architecture search (NAS) enables finding the best-performing architecture from a search space automatically. Most NAS methods exploit an over-parameterized network (i.e., a…
FYI: Flip Your Images for Dataset Distillation
Byunggwan Son, Youngmin Oh, Donghyeon Baek +1
Dataset distillation synthesizes a small set of images from a large-scale real dataset such that synthetic and real images share similar behavioral properties (e.g, distributions o…
ACLS: Adaptive and Conditional Label Smoothing for Network Calibration
Hyekang Park, Jongyoun Noh, Youngmin Oh +2
We address the problem of network calibration adjusting miscalibrated confidences of deep neural networks. Many approaches to network calibration adopt a regularization-based metho…
ALIFE: Adaptive Logit Regularizer and Feature Replay for Incremental Semantic Segmentation
Youngmin Oh, Donghyeon Baek, Bumsub Ham
We address the problem of incremental semantic segmentation (ISS) recognizing novel object/stuff categories continually without forgetting previous ones that have been learned. The…