Papers with Code 아카이브

Translation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

0

평가표

4

보존된 평가 기록

5

코드

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

https://github.com/svakulenk0/response_eval

Attention Is All You Need

https://github.com/svikramank/pix2pix

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/svjan5/CNN-for-text-classification

Word and Phrase Translation with word2vec

https://github.com/swasun/VQ-VAE-images

Theory and Experiments on Vector Quantized Autoencoders

https://github.com/sweta20/incontextmt

In-context Examples Selection for Machine Translation

https://github.com/swimmingCreative/DeepSpeechRecognition-master

Attention Is All You Need

https://github.com/sy-sunmoon/Clever-Commenter-Let-s-Try-More-Apps

Style Transfer from Non-Parallel Text by Cross-Alignment

https://github.com/symmetryadvantage/wyckofftransformer

Crystal Diffusion Variational Autoencoder for Periodic Material Generation

https://github.com/sysu-yanglab/SCROP

Predicting Retrosynthetic Reaction using Self-Corrected Transformer Neural Networks

https://github.com/t04glovern/selfie2anime

U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation

https://github.com/taasnim/unsup-word-translation

Revisiting Adversarial Autoencoder for Unsupervised Word Translation with Cycle Consistency and Improved Training

https://github.com/tahasamavati/Cycle-GAN-Tensorflow

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

https://github.com/taivu1998/GANime

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/tajwarabraraleef/coSegGAN

Co-Generation and Segmentation for Generalized Surgical Instrument Segmentation on Unlabelled Data

https://github.com/taki0112/DRIT-Tensorflow

Diverse Image-to-Image Translation via Disentangled Representations

https://github.com/taki0112/FUNIT-Tensorflow

Few-Shot Unsupervised Image-to-Image Translation

https://github.com/taki0112/GDWCT-Tensorflow

Image-to-Image Translation via Group-wise Deep Whitening-and-Coloring Transformation

https://github.com/taki0112/MUNIT-Tensorflow

Multimodal Unsupervised Image-to-Image Translation

https://github.com/taki0112/SDIT-Tensorflow

SDIT: Scalable and Diverse Cross-domain Image Translation

https://github.com/taki0112/StarGAN-Tensorflow

StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

https://github.com/taki0112/StarGAN_v2-Tensorflow

StarGAN v2: Diverse Image Synthesis for Multiple Domains

https://github.com/taki0112/Toward_spatial_unbiased-Tensorflow

Toward Spatially Unbiased Generative Models

https://github.com/taki0112/UNIT-Tensorflow

Unsupervised Image-to-Image Translation Networks

https://github.com/tangg555/sabart

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

https://github.com/tanjeffreyz/attention-is-all-you-need

Attention Is All You Need

https://github.com/tanyuqian/aspect-based-summarization

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

https://github.com/tanyuqian/texar-pytorch

Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation

https://github.com/tarujg/domain-adapt

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

https://github.com/tatp22/linformer-pytorch

Attention Is All You Need

https://github.com/tatp22/multidim-positional-encoding

Attention Is All You Need

https://github.com/tbmoon/LANL_Earthquake_Prediction

Attention Is All You Need

https://github.com/tbullmann/imagesegmentation-tensorflow

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/tbullmann/imagetranslation-tensorflow

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/tcnguyen/bert

Attention Is All You Need

https://github.com/tcxdgit/MachineTranslation_t2t

Attention Is All You Need

https://github.com/tdiggelm/nn-experiments

Pervasive Attention: 2D Convolutional Neural Networks for Sequence-to-Sequence Prediction

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