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  • Seoul, Republic of Korea

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Achievement: Pull SharkAchievement: YOLO

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Achievement: Pull SharkAchievement: YOLO

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KimRass/README.md

1. Personal Projects

1) From-scratch PyTorch Implementations of AI papers

연도 논문 내용
Vision
2014 VAE (Kingma and Welling) [✓] Training on MNIST
[✓] Visualizing Encoder output
[✓] Visualizing Decoder output
[✓] Reconstructing image
2015 CAM (Zhou et al.) [✓] Applying GoogLeNet
[✓] Generating 'Class Activatio Map'
[✓] Generating bounding box
2016 Gatys et al. [✓] Experimenting on input image size
[✓] Experimenting on VGGNet-19 with Batch normalization
[✓] Applying VGGNet-19
YOLO (Redmon et al.) [✓] Model architecture
[✓] Visualizing ground truth on grid
[✓] Visualizing model output
[✓] Visualizing class probability map
[ㅤ] Loss function
[ㅤ] Training on VOC 2012
DCGAN (Radford et al.) [✓] Training on CelebA at 64 × 64
[✓] Sampling
[✓] Interpolating in latent space
[ㅤ] Training on CelebA at 32 × 32
Noroozi et al. [✓] Model architecture
[✓] Chromatic aberration
[✓] Permutation set
Zhang et al. [✓] Visualizing empirical probability distribution
[ㅤ] Model architecture
[ㅤ] Loss function
[ㅤ] Training
2014
2017		 Conditional GAN (Mirza et al.)
WGAN-GP (Gulrajani et al.)		 [✓] Training on MNIST
2016
2017		 VQ-VAE (Oord et al.)
PixelCNN (Oord et al.)		 [✓] Training on Fashion MNIST
[✓] Training on CIFAR-10
[✓] Sampling
2017 Pix2Pix (Isola et al.) [✓] Experimenting on image mean and std
[✓] Experimenting on nn.InstanceNorm2d()
[✓] Training on Google Maps
[✓] Training on Facades
[ㅤ] higher resolution input image
CycleGAN (Zhu et al.) [✓] Experimenting on random image pairing
[✓] Experimenting on LSGANs
[✓] Training on monet2photo
[✓] Training on vangogh2photo
[✓] Training on cezanne2photo
[✓] Training on ukiyoe2photo
[✓] Training on horse2zebra
[✓] Training on summer2winter_yosemite
2018 PGGAN (Karras et al.) [✓] Experimenting on image mean and std
[✓] Training on CelebA-HQ at 512 × 512
[✓] Sampling
DeepLabv3 (Chen et al.) [✓] Training on VOC 2012
[✓] Predicting on VOC 2012 validation set
[✓] Average mIoU
[✓] Visualizing model output
RotNet (Gidaris et al.) [✓] Visualizing Attention map
StarGAN (Yunjey Choi et al.) [✓] Model architecture
2020 STEFANN (Roy et al.) [✓] FANnet architecture
[✓] Colornet architecture
[✓] Training FANnet on Google Fonts
[✓] Custom Google Fonts dataset
[✓] Average SSIM
[ㅤ] Training Colornet
DDPM (Ho et al.) [✓] Training on CelebA at 32 × 32
[✓] Training on CelebA at 64 × 64
[✓] Visualizing denoising process
[✓] 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
[✓] Visualizing Attention map using Attention Roll-out
[✓] Visualizing position embedding similarity
[✓] Interpolating position embedding
[✓] 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
[ㅤ] Bipartite matching & loss
[ㅤ] Batch normalization freezing
[ㅤ] Training on COCO 2017
2021 Improved DDPM (Nichol and Dhariwal) [✓] Cosine diffusion schedule
Classifier-Guidance (Dhariwal and Nichol) [✓] Training on CIFAR-10
[ㅤ] AdaGN
[ㅤ] BiGGAN Upsample/Downsample
[ㅤ] Improved DDPM sampling
[ㅤ] Conditional/Unconditional models
[ㅤ] Super-resolution model
[ㅤ] 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
[✓] Applying CelebA at 64 × 64
MAE (He et al.) [✓] Model architecture for self-supervised pre-training
[✓] Model architecture for classification
[ㅤ] Self-supervised pre-training on ImageNet-1K
[ㅤ] Fine-tuning on ImageNet-1K
[ㅤ] Linear probing
Copy-Paste (Ghiasi et al.) [✓] COCO dataset processing
[✓] Large scale jittering
[✓] Copy-Paste (within mini-batch)
[✓] Visualizing data
[ㅤ] 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
[✓] Visualizing position encoding
2019 BERT (Devlin et al.) [✓] Model architecture
[✓] Masked language modeling
[✓] BookCorpus data processing
[✓] SQuAD data processing
[✓] SWAG data processing
Sentence-BERT (Reimers et al.) [✓] Classification loss
[✓] Regression loss
[✓] Constrastive loss
[✓] STSb data processing
[✓] WikiSection data processing
[ㅤ] NLI data processing
RoBERTa (Liu et al.) [✓] BookCorpus data processing
[✓] Masked language modeling
[ㅤ] BookCorpus data processing ('SEGMENT-PAIR' + NSP)
[ㅤ] BookCorpus data processing ('SENTENCE-PAIR' + NSP)
[✓] BookCorpus data processing ('FULL-SENTENCES')
[ㅤ] BookCorpus data 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
[ㅤ] 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)

2) Fine-tuning 'EasyOCR' on the '공공행정문서 OCR' Dataset Provided by 'AI-Hub'

3) Recognizing Book Content Using the 'CLOVA OCR API'

4) A Rule-based Algorithm for Solving Edge-matching Puzzles of Arbitrary Sizes Using L2 Distance

5) A 'FastAPI'-based API for Performing Semantic Segmentation Using a 'DeepLabv3' Pretrained on the 'VOC2012' dataset

Pinned Loading

  1. train_easyocr train_easyocr Public

    Fine-tuning 'EasyOCR' on the '공공행정문서 OCR' dataset provided by 'AI-Hub'.

    Python 11 6

  2. PGGAN PGGAN Public

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

    Python 3

  3. ViT ViT Public

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

    Python 4

  4. CycleGAN CycleGAN Public

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

    Python 2

  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