47 citations · 55 across the 4 of their papers we have counts for
5 papers
IB-GAN: Disentangled Representation Learning with Information Bottleneck Generative Adversarial Networks
Insu Jeon, Wonkwang Lee, Myeongjang Pyeon +1
We propose a new GAN-based unsupervised model for disentangled representation learning. The new model is discovered in an attempt to utilize the Information Bottleneck (IB) framewo…
How to Move Your Dragon: Text-to-Motion Synthesis for Large-Vocabulary Objects
Wonkwang Lee, Jongwon Jeong, Taehong Moon +4
Motion synthesis for diverse object categories holds great potential for 3D content creation but remains underexplored due to two key challenges: (1) the lack of comprehensive moti…
Multi-Task Neural Processes
Donggyun Kim, Seongwoong Cho, Wonkwang Lee +1
Neural Processes (NPs) consider a task as a function realized from a stochastic process and flexibly adapt to unseen tasks through inference on functions. However, naive NPs can mo…
Revisiting Hierarchical Approach for Persistent Long-Term Video Prediction
Wonkwang Lee, Whie Jung, Han Zhang +6
Learning to predict the long-term future of video frames is notoriously challenging due to inherent ambiguities in the distant future and dramatic amplifications of prediction erro…
High-Fidelity Synthesis with Disentangled Representation
Wonkwang Lee, Donggyun Kim, Seunghoon Hong +1
Learning disentangled representation of data without supervision is an important step towards improving the interpretability of generative models. Despite recent advances in disent…