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MingTao(陶明)'s Projects

annotated_deep_learning_paper_implementations icon annotated_deep_learning_paper_implementations

🧑‍🏫 50! Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

bert-as-service icon bert-as-service

Mapping a variable-length sentence to a fixed-length vector using BERT model

bigraphgan icon bigraphgan

[BMVC 2020 Oral] Bipartite Graph Reasoning GANs for Person Image Generation

blended-diffusion icon blended-diffusion

Official implementation for "Blended Diffusion for Text-driven Editing of Natural Images" [CVPR 2022]

blip icon blip

PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

bminf icon bminf

Low-cost Inference Package for Big Pretrained Language Models (PLMs)

cdial-gpt icon cdial-gpt

A Large-scale Chinese Short-Text Conversation Dataset and Chinese pre-training dialog models

cgmh icon cgmh

Codes for <CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling>

chinese-bert-wwm icon chinese-bert-wwm

Pre-Training with Whole Word Masking for Chinese BERT(中文BERT-wwm系列模型)

clip icon clip

Contrastive Language-Image Pretraining

clip-guided-diffusion icon clip-guided-diffusion

Just playing with getting CLIP Guided Diffusion running locally, rather than having to use colab.

clipbert icon clipbert

[CVPR 2021 Best Student Paper Honorable Mention, Oral] Official PyTorch code for ClipBERT, an efficient framework for end-to-end learning on image-text and video-text tasks.

coin icon coin

[ACM MM 2024] A fast and effective Story Visualization and Continuation Model

dall-e icon dall-e

PyTorch package for the discrete VAE used for DALL·E.

de-net icon de-net

[AAAI 2023] Dynamic Text-guided Image Editing Adversarial Networks

deeplearning-500-questions icon deeplearning-500-questions

深度学习500问,以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述,以帮助自己及有需要的读者。 全书分为18个章节,50余万字。由于水平有限,书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作,联系[email protected] 版权所有,违权必究 Tan 2018.06

denseclip icon denseclip

[CVPR 2022] DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting

df-gan icon df-gan

[CVPR2022 oral] A Simple and Effective Baseline for Text-to-Image Synthesis

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