Efficient Image Gallery Representations at Scale Through Multi-Task Learning

Benjamin Gutelman; Pavel Levin

doi:10.1145/3397271.3401433

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arXiv · 2005.09027

Efficient Image Gallery Representations at Scale Through Multi-Task Learning

Benjamin Gutelman·Pavel Levin

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Abstract

Image galleries provide a rich source of diverse information about a product which can be leveraged across many recommendation and retrieval applications. We study the problem of building a universal image gallery encoder through multi-task learning (MTL) approach and demonstrate that it is indeed a practical way to achieve generalizability of learned representations to new downstream tasks. Additionally, we analyze the relative predictive performance of MTL-trained solutions against optimal and substantially more expensive solutions, and find signals that MTL can be a useful mechanism to address sparsity in low-resource binary tasks.

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Benjamin Gutelman, Pavel Levin. 2020-05-18. Efficient Image Gallery Representations at Scale Through Multi-Task Learning. https://doi.org/10.1145/3397271.3401433

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