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Poster
Subsidiary Prototype Alignment for Universal Domain Adaptation
Jogendra Nath Kundu · Suvaansh Bhambri · Akshay R Kulkarni · Hiran Sarkar · Varun Jampani · Venkatesh Babu R

Thu Dec 01 09:00 AM -- 11:00 AM (PST) @ Hall J #609
in Poster Session 5 »

Universal Domain Adaptation (UniDA) deals with the problem of knowledge transfer between two datasets with domain-shift as well as category-shift. The goal is to categorize unlabeled target samples, either into one of the "known" categories or into a single "unknown" category. A major problem in UniDA is negative transfer, i.e. misalignment of "known" and "unknown" classes. To this end, we first uncover an intriguing tradeoff between negative-transfer-risk and domain-invariance exhibited at different layers of a deep network. It turns out we can strike a balance between these two metrics at a mid-level layer. Towards designing an effective framework based on this insight, we draw motivation from Bag-of-visual-Words (BoW). Word-prototypes in a BoW-like representation of a mid-level layer would represent lower-level visual primitives that are likely to be unaffected by the category-shift in the high-level features. We develop modifications that encourage learning of word-prototypes followed by word-histogram based classification. Following this, subsidiary prototype-space alignment (SPA) can be seen as a closed-set alignment problem, thereby avoiding negative transfer. We realize this with a novel word-histogram-related pretext task to enable closed-set SPA, operating in conjunction with goal task UniDA. We demonstrate the efficacy of our approach on top of existing UniDA techniques, yielding state-of-the-art performance across three standard UniDA and Open-Set DA object recognition benchmarks.

Keywords: Object Recognition · Universal Domain Adaptation · Domain Adaptation · Bag of visual words

Author Information

Jogendra Nath Kundu (Indian Institute of Science)
Suvaansh Bhambri (Indian Institute of Science, Dhirubhai Ambani Institute Of Information and Communication Technology)
Akshay R Kulkarni (UC San Diego, Indian Institute of Science)
Hiran Sarkar (Netaji Subhash Engineering College)
Varun Jampani (Google Research)
Venkatesh Babu R (Indian Institute of Science)

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