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cs.LG2024
Content-Style Learning from Unaligned Domains: Identifiability under Unknown Latent Dimensions
Sagar Shrestha, Xiao Fu
Understanding identifiability of latent content and style variables from unaligned multi-domain data is essential for tasks such as domain translation and data generation. Existing…
cs.LG2024
Identifiable Shared Component Analysis of Unpaired Multimodal Mixtures
Subash Timilsina, Sagar Shrestha, Xiao Fu
A core task in multi-modal learning is to integrate information from multiple feature spaces (e.g., text and audio), offering modality-invariant essential representations of data.…
cs.LG2024★ 1 cited
Towards Identifiable Unsupervised Domain Translation: A Diversified Distribution Matching Approach
Sagar Shrestha, Xiao Fu
Unsupervised domain translation (UDT) aims to find functions that convert samples from one domain (e.g., sketches) to another domain (e.g., photos) without changing the high-level…