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A Minimax Game for Instance based Selective Transfer Learning

Published: 25 July 2019 Publication History

Abstract

Deep neural network based transfer learning has been widely used to leverage information from the domain with rich data to help domain with insufficient data. When the source data distribution is different from the target data, transferring knowledge between these domains may lead to negative transfer. To mitigate this problem, a typical way is to select useful source domain data for transferring. However, limited studies focus on selecting high-quality source data to help neural network based transfer learning. To bridge this gap, we propose a general Minimax Game based model for selective Transfer Learning (MGTL). More specifically, we build a selector, a discriminator and a TL module in the proposed method. The discriminator aims to maximize the differences between selected source data and target data, while the selector acts as an attacker to selected source data that are close to the target to minimize the differences. The TL module trains on the selected data and provides rewards to guide the selector. Those three modules play a minimax game to help select useful source data for transferring. Our method is also shown to speed up the training process of the learning task in the target domain than traditional TL methods. To the best of our knowledge, this is the first to build a minimax game based model for selective transfer learning. To examine the generality of our method, we evaluate it on two different tasks: item recommendation and text retrieval. Extensive experiments over both public and real-world datasets demonstrate that our model outperforms the competing methods by a large margin. Meanwhile, the quantitative evaluation shows our model can select data which are close to target data. Our model is also deployed in a real-world system and significant improvement over the baselines is observed.

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cover image ACM Conferences
KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
July 2019
3305 pages
ISBN:9781450362016
DOI:10.1145/3292500
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Published: 25 July 2019

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Author Tags

  1. gan
  2. instance-based transfer learning
  3. reinforcement learning

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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  • (2024)Simultaneous Selection and Adaptation of Source Data via Four-Level OptimizationTransactions of the Association for Computational Linguistics10.1162/tacl_a_0065812(449-466)Online publication date: 3-May-2024
  • (2024)Effective Utilization of Large-scale Unobserved Data in Recommendation SystemsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680067(5070-5077)Online publication date: 21-Oct-2024
  • (2024)Robust Knowledge Adaptation for Dynamic Graph Neural NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.338845336:11(6920-6933)Online publication date: Nov-2024
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