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Selecting the most appropriate data examples to present a deep neural network(DNN) at different stages of training is an unsolved challenge. Thoughpractitioners typically ignore this problem, a non-trivial data schedulingmethod may result in a significant improvement in both convergence andgeneralization performance. In this paper, we introduce Self-Paced Learningwith Adaptive Deep Visual Embeddings (SPL-ADVisE), a novel end-to-end trainingprotocol that unites self-paced learning (SPL) and deep metric learning (DML).We leverage the Magnet Loss to train an embedding convolutional neural network(CNN) to learn a salient representation space. The student CNN classifierdynamically selects similar instance-level training examples to form amini-batch, where the easiness from the cross-entropy loss and the truediverseness of examples from the learned metric space serve as sampleimportance priors. To demonstrate the effectiveness of SPL-ADVisE, we use deepCNN architectures for the task of supervised image classification on severalcoarse- and fine-grained visual recognition datasets. Results show that, acrossall datasets, the proposed method converges faster and reaches a higher finalaccuracy than other SPL variants, particularly on fine-grained classes.

Self-Paced Learning with Adaptive Deep Visual Embeddings