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/samarthshukla/ws-i2i
Extremely Weak Supervised Image-to-Image Translation for Semantic Segmentation
https://github.com/sambit9238/deep_text_corrector
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/samialabed/method-name-prediction
A Convolutional Attention Network for Extreme Summarization of Source Code
https://github.com/samim23/NeuralTalkAnimator
https://github.com/samnguyen8991/Facebook-MUSE
https://github.com/samsucik/t2t
https://github.com/samsunglabs/myqasr
FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech
https://github.com/samuel-yeom/fliptest
https://github.com/samuelchassot/FUNIT
https://github.com/samuellundberg/NeuralArchitectureSearch
https://github.com/samwisegamjeee/pytorch-transformers
https://github.com/samyakjain0112/Video-denoising_tensorflow-core-api-2.0
https://github.com/sangyun884/CinCGAN-pytorch
Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial Networks
https://github.com/sangyun884/Face2Webtoon
https://github.com/sangyun884/WebtoonFaces
https://github.com/sangyun884/fast-ode
Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
https://github.com/sanjsvk/CycleGANs
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/sanxing-chen/NMT2017-ZH-EN
Achieving Human Parity on Automatic Chinese to English News Translation
https://github.com/saparina/text2sql-nlvariation
Natural SQL: Making SQL Easier to Infer from Natural Language Specifications
https://github.com/saraswat/munkhdalai-nse
https://github.com/sarthaxxxxx/Attention-is-all-you-need
https://github.com/sathvik-sankruth/Data_Mining-CSE5334
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/sathvik-sankruth/Term-project
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/sathvikn/word_embedding_bias
https://github.com/satyajitghana/TSAI-DeepNLP-END2.0
https://github.com/saubhik/chainer-image-captioning
https://github.com/saurabhkulkarni77/BERT_multilabel
https://github.com/saurabhnlp/bert
https://github.com/sauram/Machine-Translation-using-Attention-Model
https://github.com/saviaga/faceswap
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/sawarn69/Medical-Report-Generation-Using-X-Ray-Images
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/say2sarwar/DeepAcid
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/sayakpaul/Spatial-Transformer-Networks-with-Keras
https://github.com/sbillburg/CRNN-with-STN
https://github.com/sblayush/Tensorflow-Attention
Effective Approaches to Attention-based Neural Machine Translation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.