Abstract. While deep neural networks have demonstrated competitive
results for many visual recognition and image retrieval tasks, the major
challenge lies in distinguishing similar images from different categories
(i.e., hard negative examples) while clustering images with large variations from the same category (i.e., hard positive examples). The current
state-of-the-art is to mine the most hard triplet examples from the minibatch to train the network. However, mining-based methods tend to look
into these triplets that are hard in terms of the current estimated network, rather than deliberately generating those hard triplets that really
matter in globally optimizing the network. For this purpose, we propose an adversarial network for Hard Triplet Generation (HTG) to optimize the network ability in distinguishing similar examples of different
categories as well as grouping varied examples of the same categories.
We evaluate our method on the real-world challenging datasets, such
as CUB200-2011, CARS196, DeepFashion and VehicleID datasets, and
show that our method outperforms the state-of-the-art methods signifi-
cantly