A conditional Triplet loss for few-shot learning and its application to image co-segmentation. (May 2021) - Shi, Daming; Orouskhani, Maysam; Orouskhani, Yasin
Abstract: Few-shot learning tries to solve the problems that suffer the limited number of samples. In this paper we present a novel conditional Triplet loss for solving few-shot problems using deep metric learning. While the conventional Triplet loss suffers the limitation of random sampling of triplets which leads to slow convergence in training process, our proposed network tries to distinguish between samples so that it improves the training speed. Our main contributions are two-fold. (i) We propose a conditional Triplet loss to train a deep Triplet network for deep metric embedding. The proposed Triplet loss employs a penalty–reward technique to enhance the convergence of standard Triplet loss. (ii) We improve the performance of the existing image co-segmentation model by replacing the conventional loss function by our proposed conditional Triplet loss. To demonstrate the performance of the proposed network, experiments carry out on MNIST and CIFAR. Simulation results are evaluated by AUC and Recall (sensitivity) and indicate that the proposed conditional Triplet network achieves higher accuracy in comparison to state-of-the-arts. Highlights: A conditional Triplet loss using penalty–reward approach for a deep Siamese network is proposed. The novel Triplet loss is conducted on few-shot image recognition. Ablation study shows the AUC and sensitivity of the proposed model. The conditional loss is employed to improve the convergence and performance of an existing image co-segmentation model.