Keras documentaion에 올라온 코드를 Pytorch로 코드 이전하는 스터디 입니다.
Original github address: https://github.com/keras-team/keras-io
시작 일자: 2021.03.04(목)
수료생(Previous members)
| Hyeongwon | Subin | Yonggi | Yookyung |
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| Github | Github | Github | Github |
참여자(Current members)
| Jeongsub | Jaehyuk | Sunwoo | Suzie |
|---|---|---|---|
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| Github | Github | Github | Github |
- Image segmentation with a U-Net-like architecture [Jeongseob Kim]
- 3D image classification from CT scans
- Semi-supervision and domain adaptation with AdaMatch
- Classification using Attention-based Deep Multiple Instance Learning (MIL).
- Convolutional autoencoder for image denoising [Jeongseob Kim]
- Barlow Twins for Contrastive SSL
- Image Classification using BigTransfer (BiT)
- OCR model for reading Captchas [Subin Kim]
- Compact Convolutional Transformers
- Consistency training with supervision
- Next-Frame Video Prediction with Convolutional
- Image classification with ConvMixer
- CutMix data augmentation for image classification [Jaehyuk Heo]
- Multiclass semantic segmentation using DeepLabV3+
- Monocular depth estimation [Hyeongwon Kang]
- Image classification with EANet (External Attention Transformer)
- FixRes: Fixing train-test resolution discrepancy
- Grad-CAM class activation visualization [Jaehyuk Heo]
- [Gradient Centralization for Better Training Performance] (https://github.com/hwk0702/keras2torch/tree/main/Computer_Vision/Gradient_Centralization_for_Better_Training_Performance) [Jaehyuk Heo]
- Handwriting recognition
- Image Captioning [Yonggi Jeong]
- Image classification via fine-tuning with EfficientNet
- Image classification with Vision Transformer [Jaehyuk Heo]
- Model interpretability with Integrated Gradients [Jaehyuk Heo]
- Involutional neural networks [Subin Kim]
- Keypoint Detection with Transfer Learning
- Knowledge Distillation [Jaehyuk Heo]
- Learning to Resize in Computer Vision
- Masked image modeling with Autoencoders
- Metric learning for image similarity search [Jaehyuk Heo]
- Low-light image enhancement using MIRNet
- MixUp augmentation for image classification
- Image classification with modern MLP models
- MobileViT: A mobile-friendly Transformer-based model for image classification
- Near-duplicate image search
- 3D volumetric rendering with NeRF
- Self-supervised contrastive learning with NNCLR
- Augmenting convnets with aggregated attention
- Image classification with Perceiver
- Point cloud classification with PointNet [Hyeongwon Kang]
- Point cloud segmentation with PointNet [Hyeongwon Kang]
- RandAugment for Image Classification for Improved Robustness [Yonggi Jeong]
- Few-Shot learning with Reptile
- Object Detection with RetinaNet [Jaehyuk Heo]
- Semantic Image Clustering [Yonggi Jeong]
- Semi-supervised image classification using contrastive pretraining with SimCLR [Subin Kim]
- Image similarity estimation using a Siamese Network with a contrastive loss
- Image similarity estimation using a Siamese Network with a triplet loss [Yonggi Jeong]
- Self-supervised contrastive learning with SimSiam [Jaehyuk Heo]
- Image Super-Resolution using an Efficient Sub-Pixel CNN
- Supervised Contrastive Learning [Subin Kim]
- Image classification with Swin Transformers
- Learning to tokenize in Vision Transformers
- Video Classification with Transformers + Video Vision Transformer [Hyeongwon Kang]
- Visualizing what convnets learn [Jaehyuk Heo]
- Train a Vision Transformer on small datasets
- Zero-DCE for low-light image enhancement
- Model Soup [Jaehyuk Heo]
- Finetuning ViT with LoRA [Jaehyuk Heo]
- Review Classification using Active Learning
- Sequence to sequence learning for performing number addition [Yookyung Kho]
- Bidirectional LSTM on IMDB [Jeongseob Kim]
- Character-level recurrent sequence-to-sequence model [Jeongseob Kim]
- End-to-end Masked Language Modeling with BERT [Subin Kim]
- Large-scale multi-label text classification
- Multimodal entailment [Yookyung Kho]
- Named Entity Recognition using Transformers [Subin Kim]
- English-to-Spanish translation with a sequence-to-sequence Transformer [Yookyung Kho]
- Natural language image search with a Dual Encoder [Subin Kim]
- Using pre-trained word embeddings
- Question Answering with Hugging Face Transformers [Yookyung Kho]
- Semantic Similarity with BERT [Jaehyuk Heo]
- Text classification with Switch Transformer [Subin Kim]
- Text classification with Transformer [Yookyung Kho]
- Text Extraction with BERT [Jaehyuk Heo]
- Text Generation using FNet
- TorchText introduction [Jeongseob Kim]
- Table Pre-training with TapasForMaskedLM [Yookyung Kho]
- Classification with Gated Residual and Variable Selection Networks [Hyeongwon Kang]
- Collaborative Filtering for Movie Recommendations [Hyeongwon Kang]
- Classification with Neural Decision Forests
- Imbalanced classification: credit card fraud detection
- A Transformer-based recommendation system [Hyeongwon Kang]
- Structured data learning with TabTransformer [Hyeongwon Kang]
- Structured data learning with Wide, Deep, and Cross networks
- Timeseries anomaly detection using an Autoencoder [Hyeongwon Kang]
- Timeseries classification with a Transformer model [Hyeongwon Kang]
- Traffic forecasting using graph neural networks and LSTM
- Timeseries forecasting for weather prediction [Hyeongwon Kang]
- Automatic Speech Recognition using CTC
- MelGAN-based spectrogram inversion using feature matching
- Speaker Recognition [Subin Kim]
- Automatic Speech Recognition with Transformer
- Variational AutoEncoder [Jaehyuk Heo]
- DCGAN to generate face images [Hyeongwon Kang]
- WGAN-GP overriding Model.train_step
- Neural style transfer [Subin Kim]
- Deep Dream [Jaehyuk Heo]
- Neural Style Transfer with AdaIN
- Conditional GAN [Yonggi Jeong]
- CycleGAN [Yonggi Jeong]
- Data-efficient GANs with Adaptive Discriminator Augmentation
- GauGAN for conditional image generation
- Character-level text generation with LSTM
- PixelCNN [Jeongseob Kim]
- Density estimation using Real NVP [Jeongseob Kim]
- Face image generation with StyleGAN
- Text generation with a miniature GPT [Subin Kim]
- Vector-Quantized Variational Autoencoders
- WGAN-GP with R-GCN for the generation of small molecular graphs
- Distributions_TFP_Pyro [Jeongseob Kim]
- Non-linear Independent Component Estimation (NICE) [Jeongseob Kim]
- Diffusion generative model(Tutorials) [Jeongseob Kim]
- Diffusion generative model(Examples - Swiss-roll, MNIST, F-MNIST, CELEBA) [Jeongseob Kim]
- Score based generative model(Tutorials) [Jeongseob Kim]
- Actor Critic Method [Hyeongwon Kang]
- Deep Deterministic Policy Gradient (DDPG) [Hyeongwon Kang]
- Deep Q-Learning for Atari Breakout [Hyeongwon Kang]
- Proximal Policy Optimization [Hyeongwon Kang]
- Graph attention networks for node classification
- Node Classification with Graph Neural Networks
- Message-passing neural network for molecular property prediction
- Graph representation learning with node2vec
- Fast Gradient Sign Method [Jaehyuk Heo]
- Projected Gradient Descent [Jaehyuk Heo]
- PatchCore [Jaehyuk Heo]
- Automatic Mixed Precision [Jaehyuk Heo]
- Gradient Accumulation [Jaehyuk Heo]
- Distributed Data Parallel [Jaehyuk Heo]