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Select, Label, and Mix: Learning Discriminative Invariant Feature Representations for Partial Domain Adaptation
Aadarsh Sahoo · Rameswar Panda · Rogerio Feris · Kate Saenko · Abir Das
Event URL: https://openreview.net/forum?id=jGqCI4rWAqo »

Partial domain adaptation which assumes that the unknown target label space is a subset of the source label space has attracted much attention in computer vision. Despite recent progress, existing methods often suffer from three key problems: negative transfer, lack of discriminability, and domain invariance in the latent space. To alleviate the above issues, we develop a novel 'Select, Label, and Mix' (SLM) framework that aims to learn discriminative invariant feature representations for partial domain adaptation. First, we present an efficient "select" module that automatically filters out the outlier source samples to avoid negative transfer while aligning distributions across both domains. Second, the "label" module iteratively trains the classifier using both the labeled source domain data and the generated pseudo-labels for the target domain to enhance the discriminability of the latent space. Finally, the "mix" module utilizes domain mixup jointly with the other two modules to explore more intrinsic structures across domains leading to a domain-invariant latent space for partial domain adaptation. Experiments on two datasets demonstrate the superiority of our framework over state-of-the-art methods.

Author Information

Aadarsh Sahoo (Indian Institute of Technology Kharagpur)
Rameswar Panda (MIT-IBM Watson AI Lab)
Rogerio Feris (MIT-IBM Watson AI Lab, IBM Research)
Kate Saenko (Boston University & MIT-IBM Watson AI Lab, IBM Research)
Kate Saenko

Kate is an AI Research Scientist at FAIR, Meta and a Full Professor of Computer Science at Boston University (currently on leave) where she leads the Computer Vision and Learning Group. Kate received a PhD in EECS from MIT and did postdoctoral training at UC Berkeley and Harvard. Her research interests are in Artificial Intelligence with a focus on out-of-distribution learning, dataset bias, domain adaptation, vision and language understanding, and other topics in deep learning. Past academic positions Consulting professor at the MIT-IBM Watson AI Lab 2019-2022. Assistant Professor, Computer Science Department at UMass Lowell Postdoctoral Researcher, International Computer Science Institute Visiting Scholar, UC Berkeley EECS Visiting Postdoctoral Fellow, SEAS, Harvard University

Abir Das (Indian Institute of Technology Kharagpur)

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