Intensive reading of Swin Transformer papers - station B up: learning AI explanation notes from Li Mu - Code World

Intensive reading of Swin Transformer papers - station B up: learning AI explanation notes from Li Mu

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Origin blog.csdn.net/Snakehj/article/details/130874322

Intensive reading of Swin Transformer papers - station B up: learning AI explanation notes from Li Mu

Li Mu's intensive reading paper: Swin transformer: Hierarchical vision transformer using shifted windows

Intensive Reading of Transformer Papers

Intensive reading of deep learning papers [14]: Vision Transformer

Lecture intensive des articles de Swin Transformer - station B up : apprentissage des notes d'explication sur l'IA de Li Mu

Intensive reading of Li Mu's paper: ResNet "Deep Residual Learning for Image Recognition" by Kaiming He

Paper reading notes: Swin-Transformer

Intensive reading of Li Mu's papers series five: DALL E2 (cross-talk on generative models, from GANs, VE/VAE/VQ-VAE/DALL E to diffusion model DDPM/ADM)

Intensive reading series of Li Mu's papers three: MoCo, contrastive learning overview (MoCov1/v2/v3, SimCLR v1/v2, DINO, etc.)

Intensive reading of deep learning papers [8]: ParseNet

Intensive reading of deep learning papers [7]: nnUNet

Intensive reading of Transformer papers - Attention Is All You Need

Intensive reading series of Li Mu's papers four: CLIP and improvement work series lectures (LSeg, GroupViT, VLiD, GLIPv1, GLIPv2, CLIPasso)

[Thesis Notes] Swin-Transformer Series Reading Notes

Swin transformer code explanation

Intensive Reading of SIMBAR Papers

Intensive reading of GAN papers

[Intensive reading of papers] CSET - Big AI Potential of Small Data

Intensive reading of deep learning papers [11]: Deeplab v2

Intensive reading of deep learning papers [6]: UNet++

Li Mu's intensive reading paper: Swin transformer: Hierarchical vision transformer using shifted windows

Li Mu's intensive reading paper: MAE "Masked Autoencoders Are Scalable Vision Learners"

Intensive reading of Li Mu's paper: BERT "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding"

Li Mu's intensive reading paper: DETR End to End Object Detection with Transformers

Detailed explanation of Swin-Transformer

Intensive Reading of YOLO Series Papers

Transfomrer Benchmark3090 vs. 3090Ti (simplified version of learning AI homework from Li Mu)

The concept of axis - follow Li Mu to learn AI course notes (supplement)

[Paper Notes] "Blockchained On-Device Federated Learning" Intensive Reading Notes

Detailed explanation of Transformer (Li Mu's text version, full of dry goods!)

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