Feb. 12, 2024, 5:42 a.m. | Kecheng Chen Elena Gal Hong Yan Haoliang Li

cs.LG updates on arXiv.org arxiv.org

In this work, we propose to tackle the problem of domain generalization in the context of \textit{insufficient samples}. Instead of extracting latent feature embeddings based on deterministic models, we propose to learn a domain-invariant representation based on the probabilistic framework by mapping each data point into probabilistic embeddings. Specifically, we first extend empirical maximum mean discrepancy (MMD) to a novel probabilistic MMD that can measure the discrepancy between mixture distributions (i.e., source domains) consisting of a series of latent distributions …

context cs.cv cs.lg data domain embeddings feature framework learn mapping mean representation samples small small data work

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