Hard-Aware Deeply Cascaded Embedding
This addresses the problem of optimizing deep metric learning efficiently for researchers and practitioners, though it is incremental as it builds on existing hard example mining techniques.
The paper tackles the challenge of hard example mining in deep metric learning by proposing a cascaded ensemble of models with varying complexities that adaptively selects hard examples for training. The method achieves state-of-the-art performance on multiple datasets, outperforming existing approaches by a large margin.
Riding on the waves of deep neural networks, deep metric learning has also achieved promising results in various tasks using triplet network or Siamese network. Though the basic goal of making images from the same category closer than the ones from different categories is intuitive, it is hard to directly optimize due to the quadratic or cubic sample size. To solve the problem, hard example mining which only focuses on a subset of samples that are considered hard is widely used. However, hard is defined relative to a model, where complex models treat most samples as easy ones and vice versa for simple models, and both are not good for training. Samples are also with different hard levels, it is hard to define a model with the just right complexity and choose hard examples adequately. This motivates us to ensemble a set of models with different complexities in cascaded manner and mine hard examples adaptively, a sample is judged by a series of models with increasing complexities and only updates models that consider the sample as a hard case. We evaluate our method on CARS196, CUB-200-2011, Stanford Online Products, VehicleID and DeepFashion datasets. Our method outperforms state-of-the-art methods by a large margin.