**CurricularFace**, or **Adaptive Curriculum Learning**, is a method for face recognition that embeds the idea of curriculum learning into the loss function to achieve a new training scheme. This training scheme mainly addresses easy samples in the early training stage and hard ones in the later stage. Specifically, CurricularFace adaptively adjusts the relative importance of easy and hard samples during different training stages.

**Self-Attention Network** (**SANet**) proposes two variations of self-attention used for image recognition: 1) pairwise self-attention which generalizes standard [dot-product attention](https://paperswithcode.com/method/dot-product-attention) and is fundamentally a set operator, and 2) patchwise self-attention which is strictly more powerful than [convolution](https://paperswithcode.com/method/convolution).

SANet

Exploring Self-attention for Image Recognition

CurricularFace

CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition

**Pyramid Vision Transformer v2** (PVTv2) is a type of [Vision Transformer](https://paperswithcode.com/method/vision-transformer) for detection and segmentation tasks. It improves on [PVTv1](https://paperswithcode.com/method/pvt) through several design improvements: (1) overlapping patch embedding, (2) convolutional feed-forward networks, and (3) linear complexity attention layers that are orthogonal to the PVTv1 framework.

Source	CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com