62 research outputs found
Robust Class-Conditional Distribution Alignment for Partial Domain Adaptation
Unwanted samples from private source categories in the learning objective of
a partial domain adaptation setup can lead to negative transfer and reduce
classification performance. Existing methods, such as re-weighting or
aggregating target predictions, are vulnerable to this issue, especially during
initial training stages, and do not adequately address class-level feature
alignment. Our proposed approach seeks to overcome these limitations by delving
deeper than just the first-order moments to derive distinct and compact
categorical distributions. We employ objectives that optimize the intra and
inter-class distributions in a domain-invariant fashion and design a robust
pseudo-labeling for efficient target supervision. Our approach incorporates a
complement entropy objective module to reduce classification uncertainty and
flatten incorrect category predictions. The experimental findings and ablation
analysis of the proposed modules demonstrate the superior performance of our
proposed model compared to benchmarks
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