On Improving the Algorithm-, Model-, and Data- Efficiency of Self-Supervised Learning
This addresses the lack of flexibility and feasibility in practical applications of SSL, offering an incremental improvement in efficiency.
The paper tackled the computational expense and reliance on multiple augmentations in self-supervised learning by proposing an efficient single-branch method based on non-parametric instance discrimination, which outperformed baselines with significantly less overhead, especially for limited data and small models.
Self-supervised learning (SSL) has developed rapidly in recent years. However, most of the mainstream methods are computationally expensive and rely on two (or more) augmentations for each image to construct positive pairs. Moreover, they mainly focus on large models and large-scale datasets, which lack flexibility and feasibility in many practical applications. In this paper, we propose an efficient single-branch SSL method based on non-parametric instance discrimination, aiming to improve the algorithm, model, and data efficiency of SSL. By analyzing the gradient formula, we correct the update rule of the memory bank with improved performance. We further propose a novel self-distillation loss that minimizes the KL divergence between the probability distribution and its square root version. We show that this alleviates the infrequent updating problem in instance discrimination and greatly accelerates convergence. We systematically compare the training overhead and performance of different methods in different scales of data, and under different backbones. Experimental results show that our method outperforms various baselines with significantly less overhead, and is especially effective for limited amounts of data and small models.