Supplementary Material – PUnDA: Probabilistic Unsupervised Domain Adaptation for Knowledge Transfer Across Visual Categories
نویسندگان
چکیده
This Supplement contains additional materials related to the paper PUnDA: Probabilistic Unsupervised Domain Adaptation for Knowledge Transfer Across Visual Categories. In particular, in Sec. 2 we present additional results that qualitatively demonstrate the advantages of our adaptation framework over competing approaches such as the ILS [2]. We contrast 2D embeddings of the features adapted by PUnDA to those of the pre-adapted fc6 layer and the ILS. We include detailed derivation of the Variational Bayes algorithm for PUnDA in Sec. 4. Finally, in Sec. 5, we provide the computational complexity analysis of our VB algorithm.
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