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KimRass/README.md

1. From-scratch PyTorch Implementations

분류 및 연도 이름 저자 구현 내용
Vision
2014 VAE Kingma and Welling [✓] Training on MNIST
[✓] Encoder output visualization
[✓] Decoder output visualization
2015 CAM Zhou et al. [✓] Application to GoogleNet
[✓] Bounding box generation from Class Activation Map
2016 Gatys et al., 2016 (image style transfer) Gatys et al. [✓] Application to VGGNet-19
YOLO Redmon et al. [✗] Training on VOC 2012
[✗] Class probability map
[✗] Ground truth vlisualization on grid
DCGAN Radford et al. [✓] Training on CelebA at 64 × 64
[✓] Sampling
[✓] Latent space interpolation
Noroozi et al., 2016 Noroozi et al. [✓] Model architecture
[✓] Chromatic aberration
[✓] Permutation set
Zhang et al., 2016 (image colorization) Zhang et al. [✓] Empirical probability distribution visualization
[✗] Color space
2014
2017		 Conditional GAN
WGAN-GP		 Mirza et al.
Gulrajani et al.		 [✓] Training on MNIST
2016
2017		 VQ-VAE & PixelCNN Oord et al.
Oord et al.		 [✓] Training on Fashion MNIST
[✓] Training on CIFAR-10
2017 Pix2Pix Isola et al. [✓] Training on Google Maps
[✓] Training on Facades
[✗] Inference on larger resolution
CycleGAN Zhu et al. [✓] Training on monet2photo
[✓] Training on vangogh2photo
[✓] Training on cezanne2photo
[✓] Training on ukiyoe2photo
[✓] Training on horse2zebra
[✓] Training on summer2winter_yosemite
Noroozi et al., 2017 Noroozi et al. [✓] Constrastive loss
2018 PGGAN Karras et al. [✓] Training on CelebA-HQ at 512 × 512
DeepLab v3 Chen et al. [✓] Training on VOC 2012
[✓] Prediction on VOC 2012 validation set
[✓] Average mIoU
[✓] Model output visualization
RotNet Gidaris et al [✓] Attention map visualization
StarGAN Yunjey Choi et al. [✓] Model architecture
2020 STEFANN Roy et al. [✓] FANnet architecture
[✓] Training FANnet on Google Fonts
[✓] Custom Google Fonts dataset
[✓] Average SSIM
DDPM Ho et al. [✓] Training on CelebA at 32 × 32
[✓] Training on CelebA at 64 × 64
[✓] Denoising process visualization
[✓] Sampling using linear interpolation
[✓] Sampling using coarse-to-fine interpolation
DDIM Song et al. [✓] Normal sampling
[✓] Sampling using spherical linear interpolation
[✓] Sampling using grid interpolation
[✓] Truncated normal
ViT Dosovitskiy et al. [✓] Training on CIFAR-10
[✓] Training on CIFAR-100
[✓] Attention map visualization using Attention Roll-out
[✓] Position embedding similarity visualization
[✓] Position embedding interpolation
[✓] CutOut
[✓] CutMix
[✓] Hide-and-Seek
SimCLR Chen et al. [✓] Normalized temperature-scaled cross entropy loss
[✓] Data augmentation
[✓] Pixel intensity histogram
DETR Carion et al. [✓] Model architecture
[✗] Bipartite matching & loss
[✗] Batch normalization freezing
[✗] Data preparation
[✗] Training on COCO 2017
2021 Improved DDPM Nichol and Dhariwal [✓] Cosine diffusion schedule
Classifier-Guidance Dhariwal and Nichol [✓] Training on CIFAR-10[✗] AdaGN
[✗] BiGGAN Upsample/Downsample
[✗] Improved DDPM sampling
[✗] Conditional/Unconditional models
[✗] Super-resolution model
[✗] Interpolation
ILVR Choi et al. [✓] Sampling using single reference
[✓] Sampling using various downsampling factors
[✓] Sampling using various conditioning range
SDEdit Meng et al. [✓] User input stroke simulation
[✓] Application to CelebA at 64 × 64
MAE He et al. [✓] Model architecture for pre-training
[✗] Model architecture for self-supervised learning
[✗] Training on ImageNet-1K
[✗] Fine-tuning
[✗] Linear probing
Copy-Paste Ghiasi et al. [✓] COCO dataset processing
[✓] Large scale jittering
[✓] Copy-Paste (within mini-batch)
[✓] Data visualization
[✗] Gaussian filter
ViViT Arnab et al. [✓] 'Spatio-temporal attention' architecture
[✓] 'Factorised encoder' architecture
[✓] 'Factorised self-attention' architecture
2022 CFG Ho et al.
Language
2017 Transformer Vaswani et al. [✓] Model architecture
[✓] Position encoding visualization
2019 BERT Devlin et al. [✓] Model architecture
[✓] Masked language modeling
[✓] BookCorpus data pre-processing
[✓] SQuAD data pre-processing
[✓] SWAG data pre-processing
Sentence-BERT Reimers et al. [✓] Classification loss
[✓] Regression loss
[✓] Constrastive loss
[✓] STSb data pre-processing
[✓] WikiSection data pre-processing
[✗] NLI data pre-processing
RoBERTa Liu et al. [✓] BookCorpus data pre-processing
[✓] Masked language modeling
[✗] BookCorpus data pre-processing
(SEGMENT-PAIR + NSP)
[✗] BookCorpus data pre-processing
(SENTENCE-PAIR + NSP)
[✓] BookCorpus data pre-processing
(FULL-SENTENCES)
[✗] BookCorpus data pre-processing
(DOC-SENTENCES)
2021 Swin Transformer Liu et al. [✓] Patch partition
[✓] Patch merging
[✓] Relative position bias
[✓] Feature map padding
[✓] Self-attention in non-overlapped windows
[✗] Shifted Window based Self-Attention
2024 RoPE Su et al. [✓] Rotary Positional Embedding
Vision-Language
2021 CLIP Radford et al. [✓] Training on Flickr8k + Flickr30k
[✓] Zero-shot classification on ImageNet1k (mini)
[✓] Linear classification on ImageNet1k (mini)

Pinned Loading

  1. PGGAN PGGAN Public

    PyTorch implementation of 'PGGAN' (Karras et al., 2018) from scratch and training it on CelebA-HQ at 512 × 512

    Python 3

  2. ViT ViT Public

    PyTorch implementation of 'ViT' (Dosovitskiy et al., 2020) and training it on CIFAR-10 and CIFAR-100

    Python 3

  3. CycleGAN CycleGAN Public

    PyTorch implementation of 'CycleGAN' (Zhu et al., 2017) and training it on 6 datasets

    Python 2

  4. CLIP CLIP Public

    PyTorch implementation of 'CLIP' (Radford et al., 2021) from scratch and training it on Flickr8k + Flickr30k

    Python 6

  5. DDPM DDPM Public

    PyTorch implementation of 'DDPM' (Ho et al., 2020) and training it on CelebA 64×64

    Python 7

  6. ILVR ILVR Public

    PyTorch implementation of 'ILVR' (Choi et al., 2021) from scratch and applying it to 'DDPM' on CelebA at 64 × 64

    Python 2