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Prashnna Kumar Gyawali

2 accepted papers

2023

Continual Unsupervised Disentangling of Self-Organizing Representations

ICLR 2023top-25%

Limited progress has been made in continual unsupervised learning of representations, especially in reusing, expanding, and continually disentangling learned semantic factors across data environments. We argue that this is because existing approaches treat continually-arrived data independently, wit…

Cited by 8SourcePDFScholar
2020

PROGRESSIVE LEARNING AND DISENTANGLEMENT OF HIERARCHICAL REPRESENTATIONS

ICLR 2020spotlight

Learning rich representation from data is an important task for deep generative models such as variational auto-encoder (VAE). However, by extracting high-level abstractions in the bottom-up inference process, the goal of preserving all factors of variations for top-down generation is compromised. M…

Cited by 58SourcecodeScholar