5 papers
Enhancing Accuracy and Robustness through Adversarial Training in Class Incremental Continual Learning
Minchan Kwon, Kangil Kim
In real life, adversarial attack to deep learning models is a fatal security issue. However, the issue has been rarely discussed in a widely used class-incremental continual learni…
Feature Structure Distillation with Centered Kernel Alignment in BERT Transferring
Hee-Jun Jung, Doyeon Kim, Seung-Hoon Na +1
Knowledge distillation is an approach to transfer information on representations from a teacher to a student by reducing their difference. A challenge of this approach is to reduce…
Learning from Matured Dumb Teacher for Fine Generalization
HeeSeung Jung, Kangil Kim, Hoyong Kim +1
The flexibility of decision boundaries in neural networks that are unguided by training data is a well-known problem typically resolved with generalization methods. A surprising re…
What and When to Look?: Temporal Span Proposal Network for Video Relation Detection
Sangmin Woo, Junhyug Noh, Kangil Kim
Identifying relations between objects is central to understanding the scene. While several works have been proposed for relation modeling in the image domain, there have been many…
Tackling the Challenges in Scene Graph Generation with Local-to-Global Interactions
Sangmin Woo, Junhyug Noh, Kangil Kim
In this work, we seek new insights into the underlying challenges of the Scene Graph Generation (SGG) task. Quantitative and qualitative analysis of the Visual Genome dataset impli…