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cs.CV2025
Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS)
Zhi Li, Hau Phan, Matthew Emigh +1
Vision-language co-embedding networks, such as CLIP, provide a latent embedding space with semantic information that is useful for downstream tasks. We hypothesize that the embeddi…
cs.LG2025
Anomaly Detection via Autoencoder Composite Features and NCE
Yalin Liao, Austin J. Brockmeier
Unsupervised anomaly detection is a challenging task. Autoencoders (AEs) or generative models are often employed to model the data distribution of normal inputs and subsequently id…