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.

paper list:

• Training data-efficient image transformers & distillation through attention.
• An image is worth 16x16 words: Transformers for image recognition at scale.
• ViViT: A Video Vision Transformer.
• Is space-time attention all you need for video understanding
• Video transformer network.
• Shuffle Transformer: Rethinking Spatial Shuffle for Vision Transformer
• CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows
• What Makes for Hierarchical Vision Transformer?
• WiderNet Go Wider Instead of Deeper

paper list:

• CARAFE: Content-Aware ReAssembly of FEatures
• Involution: Inverting the Inherence of Convolution for Visual Recognition
• Pay less attention with lightweight and dynamic convolutions
• ConvBERT: Improving BERT with Span-based Dynamic Convolution
• Dynamic Region-Aware Convolution

paper list:

• Neural Text Generation with Unlikelihood Training
• Implicit Unlikelihood Training: Improving Neural Text Generation with Reinforcement Learning