Transformer-in-Transformer
AvatarGAN
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Transformer-in-Transformer | AvatarGAN | |
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4 | 1 | |
41 | 61 | |
- | - | |
0.0 | 3.8 | |
about 2 years ago | 7 months ago | |
Jupyter Notebook | Jupyter Notebook | |
Apache License 2.0 | - |
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Transformer-in-Transformer
- I Implemented Transformer in Transformer
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Hacker News top posts: Dec 6, 2021
I Implemented Transformer in Transformer\ (5 comments)
- [P] I implemented Transformer in Transformer
AvatarGAN
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What will you do with your MtgoxNFT?
Could start with a GAN produced cartoon collection. https://github.com/aakashjhawar/AvatarGAN
What are some alternatives?
poolformer - PoolFormer: MetaFormer Is Actually What You Need for Vision (CVPR 2022 Oral)
gan-vae-pretrained-pytorch - Pretrained GANs + VAEs + classifiers for MNIST/CIFAR in pytorch.
LongNet - Implementation of plug in and play Attention from "LongNet: Scaling Transformers to 1,000,000,000 Tokens"
Deep-Learning - In-depth tutorials on deep learning. The first one is about image colorization using GANs (Generative Adversarial Nets).
swarms - Orchestrate Swarms of Agents From Any Framework Like OpenAI, Langchain, and Etc for Real World Workflow Automation. Join our Community: https://discord.gg/DbjBMJTSWD
GAN-Anime-Characters - Applied several Generative Adversarial Networks (GAN) techniques such as: DCGAN, WGAN and StyleGAN to generate Anime Faces and Handwritten Digits.
principia - The Principia Rewrite
RefinementGAN - Official implementation of the paper: https://arxiv.org/abs/2108.04957
planckforth - Bootstrapping a Forth interpreter from hand-written tiny ELF binary. Just for fun.
nn - 🧑🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
Fast-Transformer - An implementation of Fastformer: Additive Attention Can Be All You Need, a Transformer Variant in TensorFlow
faceswap-GAN - A denoising autoencoder + adversarial losses and attention mechanisms for face swapping.