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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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5

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/awjuliani/Pix2Pix-Film

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/awsm-research/autotransform-replication

Training Tips for the Transformer Model

https://github.com/aymericdamien/TopDeepLearning

Simple Recurrent Units for Highly Parallelizable Recurrence

https://github.com/aynetdia/mtp_curriculum

Blockwise Parallel Decoding for Deep Autoregressive Models

https://github.com/ays-dev/keras-transformer

Attention Is All You Need

https://github.com/b-etienne/Seq2seq-PyTorch

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/b2renger/Runway-experiment-img2txt2img2txt

Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge

https://github.com/babangain/translation

Scaling Neural Machine Translation

https://github.com/babylonhealth/MultilingualFactorAnalysis

Word Translation Without Parallel Data

https://github.com/babylonhealth/fastText_multilingual

Exploiting Similarities among Languages for Machine Translation

https://github.com/bagequan/tencent-transformer-with-disagreement

Agreement-based Joint Training for Bidirectional Attention-based Neural Machine Translation

https://github.com/baidu-research/DT-RAM

Recurrent Models of Visual Attention

https://github.com/baidu-research/HNN

Offline bilingual word vectors, orthogonal transformations and the inverted softmax

https://github.com/bakirillov/rgramlib

R-grams: Unsupervised Learning of Semantic Units in Natural Language

https://github.com/banayoyo/yoolact

YOLACT: Real-time Instance Segmentation

https://github.com/bangoc123/transformer

Attention Is All You Need

https://github.com/baon6052/VAE-GAN_Pegasus

Autoencoding beyond pixels using a learned similarity metric

https://github.com/baotramduong/Sentiment-Analysis-with-Deep-Learning-using-BERT

Attention Is All You Need

https://github.com/baotramduong/Twitter-Sentiment-Analysis-with-Deep-Learning-using-BERT

Attention Is All You Need

https://github.com/bareeka/ganproject

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/barnerwothers/MUSE

Word Translation Without Parallel Data

https://github.com/bartzi/chainer-transformer

Attention Is All You Need

https://github.com/basile-bron/Artificial-Intelligence-Lexical

Attention Is All You Need

https://github.com/basiralab/ABMT

Deep Graph Translation

https://github.com/basiralab/CGTS-GAN

Deep Graph Translation

https://github.com/basiralab/MultiGraphGAN

Multi-marginal Wasserstein GAN

https://github.com/basiralab/topoGAN

Multi-marginal Wasserstein GAN

https://github.com/batra-mlp-lab/vln-chasing-ghosts

Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments

https://github.com/batra-mlp-lab/vln-sim2real

Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments

https://github.com/baudm/vaegan-celebs-keras

Autoencoding beyond pixels using a learned similarity metric

https://github.com/bayer-science-for-a-better-life/data2text-bioleaflets

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

https://github.com/bcmi/Object-Shadow-Generation-Dataset-DESOBA

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/bdusell/stack-attention

Attention Is All You Need

https://github.com/beinborn/SemanticDrift

Word Translation Without Parallel Data

https://github.com/ben-davidson-6/fixup

Fixup Initialization: Residual Learning Without Normalization

https://github.com/bengittelson/sow-reap-paraphrasing

Neural Syntactic Preordering for Controlled Paraphrase Generation

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.