Learning Metrics from Teachers: Compact Networks for Image Embedding.
The paper will be published at the conference of 2019 Computer Vision and Pattern Recognition (CVPR19). An pre-print version is available.
Metric learning networks are used to compute image embeddings, which are widely used in many applications such as image retrieval and face recognition. In this paper, we propose to use network distillation to efficiently compute image embeddings with small networks. Network distillation has been successfully applied to improve image classification, but has hardly been explored for metric learning. To do so, we propose two new loss functions that model the communication of a deep teacher network to a small student network. We evaluate our system in several datasets, including CUB-200-2011, Cars-196, Stanford Online Products and show that embeddings computed using small student networks perform significantly better than those computed using standard networks of similar size. Results on a very compact network (MobileNet-0.25), which can be used on mobile devices, show that the proposed method can greatly improve Recall@1 results from 27.5% to 44.6%. Furthermore, we investigate various aspects of distillation for embeddings, including hint and attention layers, semi-supervised learning and cross quality distillation.
Lu Yu, Vacit Oguz Yazici, Xialei Liu, Joost van de Weijer, Yongmei Cheng, Arnau Ramisa
We evaluate our system in several datasets, including CUB-200-2011, Cars-196, Stanford Online Products.
The first half of classes are used for training and the remaining half for testing. For instance, on CUB-200-2011 dataset, 100 classes (5,864 images) are for training and the remaining 100 classes (5,924 images) are for testing.
Please download CUB-200-2011 , Cars-196, Stanford Online Products datasets first, the sprit_data function is then used to sprit the train and test set.
All training and test are done in Pytorch framework.
Pytorch vesion: 0.3.0.post4
Python version: 2.7
The pre-trained Teacher models on cub, cars and products are available to download.
The code of relative teacher and absolute teacher is available in the folder.
./run_main.sh