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Zoo-Tuning: Adaptive Transfer from A Zoo of Models
Yang Shu · Zhi Kou · Zhangjie Cao · Jianmin Wang · Mingsheng Long

Tue Jul 20 05:40 AM -- 05:45 AM (PDT) @ None

With the development of deep networks on various large-scale datasets, a large zoo of pretrained models are available. When transferring from a model zoo, applying classic single-model-based transfer learning methods to each source model suffers from high computational cost and cannot fully utilize the rich knowledge in the zoo. We propose \emph{Zoo-Tuning} to address these challenges, which learns to adaptively transfer the parameters of pretrained models to the target task. With the learnable channel alignment layer and adaptive aggregation layer, Zoo-Tuning \emph{adaptively aggregates channel aligned pretrained parameters to derive the target model}, which simultaneously promotes knowledge transfer and adapts source models to downstream tasks. The adaptive aggregation substantially reduces the computation cost at both training and inference. We further propose lite Zoo-Tuning with the temporal ensemble of batch average gating values to reduce the storage cost at the inference time. We evaluate our approach on a variety of tasks, including reinforcement learning, image classification, and facial landmark detection. Experiment results demonstrate that the proposed adaptive transfer learning approach can more effectively and efficiently transfer knowledge from a zoo of models.

Author Information

Yang Shu (Tsinghua University)
Zhi Kou (Tsinghua University)
Zhangjie Cao (Tsinghua University)
Jianmin Wang (Tsinghua University)
Mingsheng Long (Tsinghua University)

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