CoReFace：面向深度人脸识别的样本引导对比正则化
CoReFace: Sample-Guided Contrastive Regularization for Deep Face Recognition
AI总结：
针对训练分类器与开放集评估不一致的问题，本文提出 CoReFace，通过嵌入增强、自适应间隔监督对比损失及新配对协议实现图像级正则化，提升人脸识别性能。
摘要：
特征表示的判别能力是开放集人脸识别的关键。以往方法依赖分类层中表示身份的可学习权重。然而，评估过程并不学习身份表示，并且会丢弃训练得到的分类器。这种不一致可能使特征编码器难以理解评估目标，并削弱基于身份的方法的效果。为缓解上述问题，我们提出了一种名为 Contrastive Regularization for Face recognition（CoReFace，人脸识别对比正则化）的新方法，在特征表示学习中施加图像级正则化。具体而言，我们采用样本引导的对比学习，直接利用图像—图像关系对训练进行正则化，这与评估过程保持一致。为将对比学习融入人脸识别，我们对嵌入而非图像进行增强，以避免图像质量下降。随后，我们通过引入自适应间隔和监督对比掩码，提出一种用于表示分布的新型对比损失，以生成稳定的损失值并避免与分类监督信号冲突。最后，我们通过探索新的样本对耦合协议，发现并解决了对比学习中的语义重复信号问题。大量实验证明了 CoReFace 的有效性和效率，其与最先进方法相比具有很强的竞争力。
英文摘要：
The discriminability of feature representation is the key to open-set face recognition. Previous methods rely on the learnable weights of the classification layer that represent the identities. However, the evaluation process learns no identity representation and drops the classifier from training. This inconsistency could confuse the feature encoder in understanding the evaluation goal and hinder the effect of identity-based methods. To alleviate the above problem, we propose a novel approach namely Contrastive Regularization for Face recognition (CoReFace) to apply image-level regularization in feature representation learning. Specifically, we employ sample-guided contrastive learning to regularize the training with the image-image relationship directly, which is consistent with the evaluation process. To integrate contrastive learning into face recognition, we augment embeddings instead of images to avoid the image quality degradation. Then, we propose a novel contrastive loss for the representation distribution by incorporating an adaptive margin and a supervised contrastive mask to generate steady loss values and avoid the collision with the classification supervision signal. Finally, we discover and solve the semantically repetitive signal problem in contrastive learning by exploring new pair coupling protocols. Extensive experiments demonstrate the efficacy and efficiency of our CoReFace which is highly competitive with the state-of-the-art approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
