Deep Neural Networks

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Deep Residual Learning for Image Recognition
• EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• Attention Is All You Need

Self-Supervised Learning

• Representation Learning with Contrastive Predictive Coding
• A Simple Framework for Contrastive Learning of Visual Representations
• Barlow Twins: Self-Supervised Learning via Redundancy Reduction
• Bootstrap your own latent: A new approach to self-supervised Learning
• Momentum Contrast for Unsupervised Visual Representation Learning
• Emerging Properties in Self-Supervised Vision Transformers
• Contrastive Multiview Coding
• Exploring Simple Siamese Representation Learning
• Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
• VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning

Dataset Distillation

• Dataset Distillation by Matching Training Trajectories
• Accelerating Dataset Distillation via Model Augmentation
• Dataset Condensation with Distribution Matching
• Dataset Distillation via the Wasserstein Metric
• Dataset Meta-Learning from Kernel Ridge-Regression
• Dataset Distillation using Neural Feature Regression
• Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective
• On the Diversity and Realism of Distilled Dataset: An Efficient Dataset Distillation Paradigm

Generative Models

• Auto-Encoding Variational Bayes
• beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework
• Generative Adversarial Networks
• Wasserstein GAN
• Denoising Diffusion Probabilistic Models
• Denoising Diffusion Implicit Models