Bin similarity‐based domain adaptation for fine‐grained image classification

Fine-grained classification tasks are challenging because fine-grained data sets are quite scarce. Thus, we utilized the domain adaptation method to migrate knowledge from large, labeled data sets to fine-grained target data sets. We employed the bin similarity (BS) algorithm to measure and select the approximate domains from large-scale data sets to the fine-grained target domains. Source domain feature space was divided into multiple bins and the features of the target domains were sampled to fill the bins. The most similar domains were selected based on the similarity statistics of the sample features. We implemented the BS algorithm combined with the popular convolutional neural networks, pretrained the network on the selected similar subdata sets, and subsequently fine-tuned it on the fine-grained data sets. We evaluated the BS classification model on Stanford Dogs and Oxford Flower data sets, and the results showed improved BS classification performance compared with the state-of-the-art domain adaptation methods, earth mover's distance, selective joint fine-tuning, L2 with starting point, and domain similarity for transfer learning. Furthermore, BS is a pluggable module that boosts the performance of domain adaptation.