paper list:

• An efficient framework for learning sentence representations.
• CLEAR: Contrastive Learning for Sentence Representation
• Declutr: Deep contrastive learn- ing for unsupervised textual representations. arXiv
• SimCSE: Simple Contrastive Learning of Sentence Embeddings
• R-Drop: R-Drop: Regularized Dropout for Neural Networks
• Coco-lm: Correcting and contrasting text sequences for language model pretraining.
• Learning dense representations of phrases at scale.
• An unsupervised sentence embedding method by mutual information maximization.
• Representation degeneration problem in training natural language generation models
• CosReg: Improving neural language generation with spectrum control.
• CLAPS: Contrastive Learning with Adversarial Perturbations for Conditional Text Generation
• LINE: Contrastive Learning with Semantic Negative Examples for Natural Language Understanding
• Adversarial PerturbationInterpretable Adversarial Perturbation in Input Embedding Space for Text
• Contrastive Learning for Many-to-many Multilingual Neural Machine Translation
• Unsupervised Data Augmentation for Consistency Training

• simCLR
• MoCo
• BYOL
• Swin-ssl
• BraVe
• What Makes for Good Views for Contrastive Learning?
• BYOL works even without batch statistics
• Understanding Self-Supervised Learning Dynamics without Contrastive Pairs
• Big Self-Supervised Models are Strong Semi-Supervised Learners
• Understanding contrastive representation learning through alignment and uniformity on the hypersphere.