295 citations · 328 across the 10 of their papers we have counts for
9 papers · 1 filter
On the Stability-Plasticity Dilemma of Class-Incremental Learning
Dongwan Kim, Bohyung Han
A primary goal of class-incremental learning is to strike a balance between stability and plasticity, where models should be both stable enough to retain knowledge learned from pre…
Multi-Modal Representation Learning with Text-Driven Soft Masks
Jaeyoo Park, Bohyung Han
We propose a visual-linguistic representation learning approach within a self-supervised learning framework by introducing a new operation, loss, and data augmentation strategy. Fi…
Variational Distribution Learning for Unsupervised Text-to-Image Generation
Minsoo Kang, Doyup Lee, Jiseob Kim +2
We propose a text-to-image generation algorithm based on deep neural networks when text captions for images are unavailable during training. In this work, instead of simply generat…
Information-Theoretic GAN Compression with Variational Energy-based Model
Minsoo Kang, Hyewon Yoo, Eunhee Kang +3
We propose an information-theoretic knowledge distillation approach for the compression of generative adversarial networks, which aims to maximize the mutual information between te…
Towards Sequence-Level Training for Visual Tracking
Minji Kim, Seungkwan Lee, Jungseul Ok +2
Despite the extensive adoption of machine learning on the task of visual object tracking, recent learning-based approaches have largely overlooked the fact that visual tracking is…
Pooling Revisited: Your Receptive Field is Suboptimal
Dong-Hwan Jang, Sanghyeok Chu, Joonhyuk Kim +1
The size and shape of the receptive field determine how the network aggregates local information and affect the overall performance of a model considerably. Many components in a ne…