In Self-Supervised Learning (SSL), various pretext tasks are designed for learning feature representations through contrastive loss. However, previous studies have shown that this loss is less tolerant to semantically similar samples due to the inherent defect of instance discrimination objectives, which may harm the quality of learned feature embeddings used in downstream tasks. To improve the discriminative ability of feature embeddings in SSL, we propose a new loss function called Angular Contrastive Loss (ACL), a linear combination of angular margin and contrastive loss. ACL improves contrastive learning by explicitly adding an angular margin between positive and negative augmented pairs in SSL. Experimental results show that using ACL for both supervised and unsupervised learning significantly improves performance. We validated our new loss function using the FSDnoisy18k dataset, where we achieved 73.6% and 77.1% accuracy in sound event classification using supervised and self-supervised learning, respectively.

翻译：在自监督学习（SSL）中，通常设计各种前置任务，通过对比损失来学习特征表示。然而，以往研究表明，由于实例判别目标的固有缺陷，对比损失对语义相似样本的容忍度较低，这可能损害下游任务中学习到的特征嵌入的质量。为提升SSL中特征嵌入的判别能力，我们提出了一种名为角度对比损失（ACL）的新损失函数，它是角度间隔与对比损失的线性组合。ACL通过在SSL中显式地在正负增强样本对之间添加角度间隔，改进了对比学习。实验结果表明，在监督学习和无监督学习中使用ACL均能显著提升性能。我们使用FSDnoisy18k数据集验证了新损失函数，在监督学习和自监督学习的声音事件分类任务中，分别达到了73.6%和77.1%的准确率。