117 citations · 443 across the 36 of their papers we have counts for
5 papers · 1 filter
Towards More Robust Interpretation via Local Gradient Alignment
Sunghwan Joo, Seokhyeon Jeong, Juyeon Heo +2
Neural network interpretation methods, particularly feature attribution methods, are known to be fragile with respect to adversarial input perturbations. To address this, several m…
GRIT-VLP: Grouped Mini-batch Sampling for Efficient Vision and Language Pre-training
Jaeseok Byun, Taebaek Hwang, Jianlong Fu +1
Most of the currently existing vision and language pre-training (VLP) methods have mainly focused on how to extract and align vision and text features. In contrast to the mainstrea…
Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks
Hongjoon Ahn, Yongyi Yang, Quan Gan +2
Heterogeneous graph neural networks (GNNs) achieve strong performance on node classification tasks in a semi-supervised learning setting. However, as in the simpler homogeneous GNN…
Towards Diverse Evaluation of Class Incremental Learning: A Representation Learning Perspective
Sungmin Cha, Jihwan Kwak, Dongsub Shim +4
Class incremental learning (CIL) algorithms aim to continually learn new object classes from incrementally arriving data while not forgetting past learned classes. The common evalu…
Rebalancing Batch Normalization for Exemplar-based Class-Incremental Learning
Sungmin Cha, Sungjun Cho, Dasol Hwang +3
Batch Normalization (BN) and its variants has been extensively studied for neural nets in various computer vision tasks, but relatively little work has been dedicated to studying t…