Deep transfer learning for fine-grained categorization on micro datasets

Overview: Fine-grained categorization is challenging due to its small inter-class and large intra-class variance. Moreover, requiring domain expertise makes fine-grained labelled data much more expensive to acquire. Existing models predominantly require extra information such as bounding box and part annotation in addition to the image category labels, which involves heavy human manual labor. To solve this problem, we propose a novel deep transfer learning model, which transfers the learned representations from large-scale labelled fine-grained datasets to micro fine-grained datasets. While the network in deep learning is a unified training and prediction framework that combines multi-level feature extractors and recognizers, end-to-end processing is particularly important. The design concept for our model is to take full advantage of the ability that the convolutional neural network itself can perform end-to-end processing. As is known that feature transfer learning can use the existing data to rapidly construct the corresponding network parameters for new data through end-to-end training, which assumes that the source domain and the target domain contains some common cross-features, data from each domain can be transformed into the same feature space for the following learning. We present a novel discriminative training method that is used to learn similarity measurement, introducing the cohesion-domain quantitative calculation for the correlation between the two domains. Firstly, we introduce a cohesion domain to measure the degree of correlation between source domain and target domain. Secondly, selecting the transferrable feature that are suitable for the target domain based on the correlation. Finally, we make most of perspective-class labels for auxiliary learning, and learn all the attributes through joint learning to extract more feature representations. Our model aims to make joint adjustments from end to end, we expect to explore abundant source-domain attributes through cross-domain learning and capture more complex cross-domain knowledge by embedding cross-dataset information, in order to minimize the original function loss for the learning tasks in two domains as much as possible. For the problem of inter-domain transition network, we freeze part of the network layers to extract relatively more well-defined representations of labelled fine-grained samples for transferring to target domain. Since feature learning has the ability to collect hierarchical information which is not affected by the training data. In this way, the problem of high non-convex model optimization is not only simplified, but also can be modified from a more local perspective. So that subsequent incremental learning can limit the switching task to its own domain, and it is also conducive for multi-task parallel training to share the learned representation from different tasks. The experiments show that our model not only achieves high categorization accuracy but also economizes training time effectively, it also verifies the conclusion that the inter-domain feature transition can accelerate learning and optimization.