Training triplet networks with large-scale data is challenging in face recognition. Due to the number of possible triplets explodes with the number of samples, previous studies adopt the online hard negative mining(OHNM) to handle it. However, as the number of identities becomes extremely large, the training will suffer from bad local minima because effective hard triplets are difficult to be found. To solve the problem, in this paper, we propose training triplet networks with subspace learning, which splits the space of all identities into subspaces consisting of only similar identities. Combined with the batch OHNM, hard triplets can be found much easier. Experiments on the large-scale MS-Celeb-1M challenge with 100K identities demonstrate that the proposed method can largely improve the performance. In addition, to deal with heavy noise and large-scale retrieval, we also make some efforts on robust noise removing and efficient image retrieval, which are used jointly with the subspace learning to obtain the state-of-the-art performance on the MS-Celeb-1M competition (without external data in Challenge1).
To make full use of the batch, some studies @cite_1 generate hard triplets online within the batch. Hermans al. @cite_1 propose the batch OHNM, in which negative samples are searched within the batch based on their distance to the anchor, and the top nearest ones are considered as candidate hard negative samples. In this way, more hard triplets can be found easily, and the best performance is obtained in person re-identification with @math identities. Due to their small scale, the probability of sampling similar identities with an usual batchsize( @math or @math ) is large. However, in the large-scale case, randomly sampling similar identities will be much more difficult, thus the batch OHNM will fail. In this paper, we focus on how to find effective hard triplets in large-scale face recognition.
@cite_1
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In the past few years, the field of computer vision has gone through a revolution fueled mainly by the advent of large datasets and the adoption of deep convolutional neural networks for end-to-end learning. The person re-identification subfield is no exception to this. Unfortunately, a prevailing belief in the community seems to be that the triplet loss is inferior to using surrogate losses (classification, verification) followed by a separate metric learning step. We show that, for models trained from scratch as well as pretrained ones, using a variant of the triplet loss to perform end-to-end deep metric learning outperforms most other published methods by a large margin.