Related papers: Auto-Transfer: Learning to Route Transferrable Representations

Auto-Transfer: Learning to Route Transferrable Representations

URL: http://arxiv.org/abs/2202.01011v3
Date: Fri, 4 Feb 2022 03:44:03 GMT
Title: Auto-Transfer: Learning to Route Transferrable Representations
Authors: Keerthiram Murugesan, Vijay Sadashivaiah, Ronny Luss, Karthikeyan Shanmugam, Pin-Yu Chen, Amit Dhurandhar
Abstract summary: We propose a novel adversarial multi-armed bandit approach which automatically learns to route source representations to appropriate target representations. We see upwards of 5% accuracy improvements compared with the state-of-the-art knowledge transfer methods.
Score: 77.30427535329571
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Knowledge transfer between heterogeneous source and target networks and tasks has received a lot of attention in recent times as large amounts of quality labelled data can be difficult to obtain in many applications. Existing approaches typically constrain the target deep neural network (DNN) feature representations to be close to the source DNNs feature representations, which can be limiting. We, in this paper, propose a novel adversarial multi-armed bandit approach which automatically learns to route source representations to appropriate target representations following which they are combined in meaningful ways to produce accurate target models. We see upwards of 5% accuracy improvements compared with the state-of-the-art knowledge transfer methods on four benchmark (target) image datasets CUB200, Stanford Dogs, MIT67, and Stanford40 where the source dataset is ImageNet. We qualitatively analyze the goodness of our transfer scheme by showing individual examples of the important features our target network focuses on in different layers compared with the (closest) competitors. We also observe that our improvement over other methods is higher for smaller target datasets making it an effective tool for small data applications that may benefit from transfer learning.

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