100 Papers with Code
Hi, this is a (planned) collection of 100 influential research papers in AI field each paired with my implementation of the corresponding code. The goal of this project is to provide researchers, practitioners, and enthusiasts with accessible summaries of seminal works, along with practical code resources that make it easier to understand, reproduce, and apply the core ideas behind these papers. By bridging the gap between theory and practice, I hope to foster a deeper understanding of key AI concepts and facilitate innovation in the field.
02: An Image is Worth \(16 \times 16\) Words: Transformers for Image Recognition at Scale (Vision-Transformer )
Computer Vision
Transformer
06: Learning Transferable Visual Models From Natural Language Supervision (CLIP )
Multi Modality
Representation Learning
07: Emerging Properties in Self-Supervised Vision Transformers (DINO )
Self Supervised Learning
Representation Learning
08: Auto-Encoding Variational Bayes (VAE )
Self Supervised Learning
Generative Model
Representation Learning
09: Masked Autoencoders Are Scalable Vision Learners(MAE )
Self Supervised Learning
Representation Learning
AutoEncoder
11: Neural Discrete Representation Learning (VQ_VAE )
Representation Learning
Self Supervised Learning
16: Scalable Diffusion Models with Transformers (DiT )
Generative Model
Diffusion Model
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