Papers with Code 아카이브
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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 4
- 보존된 평가 기록
- 5
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ArenGolazizian/Pix2Pix-Cityscapes-Seg2Real
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/ArkaJU/Image-Colorization-CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Arthurizijar/Bert_Airport
https://github.com/ArvidWartenberg/retrosynthesis
https://github.com/AshwinDeshpande96/Hierarchical-Softmax
Strategies for Training Large Vocabulary Neural Language Models
https://github.com/AsianZeus/Ooze-Handwritten-Text-Generator
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/Aspire-Mayank/Top-Deep-Learning-Stuffs
https://github.com/AssafSinger94/sigmorphon-2020-inflection
https://github.com/Asteur/RERITES-AvgWeightDescentLSTM-PoetryGeneration
https://github.com/Atharva-Phatak/Generating-Webpages-from-Screenshots
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/Atharva-Phatak/Image2Image
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Atharva-Phatak/Season-Transfer
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/AtheMathmo/AggMo
https://github.com/AtheMathmo/lookahead-lstm
https://github.com/AtluriNikhil/Image-Captioning
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/Attn-to-FC/Attn-to-FC
code2seq: Generating Sequences from Structured Representations of Code
https://github.com/Automattic/wp-translate
https://github.com/Aveek-Saha/Transformer
https://github.com/AverageName/Cycle_gan_pytorch
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/AverageName/UI2IT
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Avmb/lowrank-gru
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
https://github.com/Awadelrahman/GANPolypSeg
Generative Adversarial Networks for Automatic Polyp Segmentation
https://github.com/AyanKumarBhunia/Self-Supervised-Learning-for-Sketch
Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting
https://github.com/BH94/cGANs-tensorflow-Python
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/BPYap/IMT-Style-Transfer
IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and Translation
https://github.com/BR-IDL/PaddleViT/blob/main/image_classification/ResMLP
ResMLP: Feedforward networks for image classification with data-efficient training
https://github.com/Babylonpartners/MultilingualFactorAnalysis
https://github.com/Babylonpartners/fastText_multilingual
Exploiting Similarities among Languages for Machine Translation
https://github.com/Baichenjia/NMT-eager
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/Baichenjia/Pix2Pix-eager
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/Baichenjia/Tensorflow-TCN
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
https://github.com/Bakikii/ganillare
GANILLA: Generative Adversarial Networks for Image to Illustration Translation
https://github.com/BalajiAI/PyTorch-GAN/tree/main/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/BangLiu/QANet-PyTorch
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
https://github.com/BasileBron/Artificial-Intelligence-Lexical
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.