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We propose an efficient transfer learning method for adapting ImageNetpre-trained Convolutional Neural Network (CNN) to fine-grained imageclassification task. Conventional transfer learning methods typically face thetrade-off between training time and accuracy. By adding "attention module" toeach convolutional filters of the pre-trained network, we are able to rank andadjust the importance of each convolutional signal in an end-to-end pipeline.In this report, we show our method can adapt a pre-trianed ResNet50 for afine-grained transfer learning task within few epochs and achieve accuracyabove conventional transfer learning methods and close to models trained fromscratch. Our model also offer interpretable result because the rank of theconvolutional signal shows which convolution channels are utilized andamplified to achieve better classification result, as well as which signalshould be treated as noise for the specific transfer learning task, which couldbe pruned to lower model size.

Pay Attention to Convolution Filters: Towards Fast and Accurate Fine-Grained Transfer Learning