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KimRass/README.md

1. Resume

RESUME

2. 'Baekjoon Online Judge' Solved Rank

hyp3rflow's solved.ac stats

3. PyTorch Implementations From Scratch
Vision
2014 VAE Kingma and Welling [✓] Training on MNIST
[✓] Encoder output visualization
[✓] Decoder output visualization
2015 CAM Zhou et al. [✓] Application to GoogleNet
2016 Gatys et al., 2016 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. [✓] Architecture
[✓] Chromatic aberration
[✓] Permutation set
Zhang et al., 2016 Zhang et al. [✓] Empirical probability distribution
[✗] Color space
2014
2017		 Conditional GAN
WGAN-GP		 Mirza et al.
Gulrajani et al.		 [✓] Training on MNIST
2016
2017		 PixelCNN
VQ-VAE		 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 Monet to photo
[✓] Training on Vangogh to photo
[✓] Training on Cezanne to photo
[✓] Training on Ukiyo-e to photo
[✓] Training on Horse to zebra
[✓] Training on Summer to winter
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
PixelLink Deng et al. [✓] Architecture
[✓] Instance-balanced cross entropy loss
[✓] Post-processing
RotNet Gidaris et al [✓] Attention map visualization
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
[✓] Linear interpolation sampling
[✓] Coarse-to-fine sampling
DDIM Song et al. [✓] Sampling
[✓] Spherical interpolation sampling
[✓] Interpolation on grid sampling
[✓] Truncated normal
ViT Dosovitskiy et al. [✓] Training on CIFAR-10
[✓] Training on CIFAR-100
[✓] Attention Roll-out
[✓] Position embedding similarity
[✓] Position embedding interpolation
Extra
[✓] CutOut
[✓] Hide-and-Seek
[✓] CutMix
SimCLR Chen et al. [✓] Normalized temperature-scaled cross entropy loss
[✓] Data augmentation
[✓] Pixel intensity histogram
DETR Carion et al. [✓] Architecture
[✗] Batch normalization freezing
[✗] Data preparation
[✗] Training on COCO 2017
2021 Improved DDPM Nichol and Dhariwal [✓] Cosine diffusion schedule
Classifier-Guidance Dhariwal and Nichol [✗] AdaGN
[✗] BiGGAN Upsample/Downsample
[✗] Improved DDPM sampling
[✗] Conditional/Unconditional models
[✗] Super-resolution model
[✗] Interpolation
ILVR Choi et al. [✓] Sampling from single reference
[✓] Sampling from various scale factors
[✓] Sampling from various conditioning range
SDEdit Meng et al. [✓] User input stroke simulation
MAE He et al. [✓] MAE architecture for pre-training
[✗] MAE architecture for self-supervised learning
[✗] Training on ImageNet-1K
[✗] Fine-tuning
[✗] Linear probing
Copy-Paste Ghiasi et al. [✓] Large scale jittering
[✓] Copy-Paste (within mini-batch)
[✗] Gaussian filter
ViViT Arnab et al.
2022 CFG Ho et al.
Language
2017 Transformer Vaswani et al. [✓] Architecture
[✓] Position encoding visualization
2019 BERT Devlin et al. [✓] BookCorpus data pre-processing
[✓] Architecture
[✓] Masked language modeling
[✓] 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)
Vision-Language
2021 CLIP Radford et al. [✓] Training on Flickr8k + Flickr30k
[✓] Zero-shot classification on ImageNet1k (mini)
[✓] Linear classification on ImageNet1k (mini)

Pinned

  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 5

  5. DDPM DDPM Public

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

    Python 6

  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 1