2 papers
cs.LG2026
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…