3 papers
cs.LG2025
Multiple Invertible and Partial-Equivariant Function for Latent Vector Transformation to Enhance Disentanglement in VAEs
Hee-Jun Jung, Jaehyoung Jeong, Kangil Kim
Disentanglement learning is central to understanding and reusing learned representations in variational autoencoders (VAEs). Although equivariance has been explored in this context…
cs.LG2024
CFASL: Composite Factor-Aligned Symmetry Learning for Disentanglement in Variational AutoEncoder
Hee-Jun Jung, Jaehyoung Jeong, Kangil Kim
Symmetries of input and latent vectors have provided valuable insights for disentanglement learning in VAEs. However, only a few works were proposed as an unsupervised method, and…
cs.CL2022
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…