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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

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평가표

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보존된 평가 기록

5

코드

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

https://github.com/gabrielStanovsky/mt_gender

Evaluating Gender Bias in Machine Translation

https://github.com/gagan16/Cyclegan-tensorflow

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

https://github.com/gahaalt/continual-learning-overview

HyperNetworks

https://github.com/gahaalt/continual-learning-with-hypernets

HyperNetworks

https://github.com/ganslate-team/ganslate

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

https://github.com/garyzhao/FRGAN

Learning to Forecast and Refine Residual Motion for Image-to-Video Generation

https://github.com/gatech-eic/s3-router

XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale

https://github.com/gavincaoyuji/NLP

Attention Is All You Need

https://github.com/gcambara/cape

CAPE: Encoding Relative Positions with Continuous Augmented Positional Embeddings

https://github.com/gcwl/pytorch-cyclegan

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

https://github.com/gcwl/pytorch-pix2pix

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/gentaiscool/end2end-asr-pytorch

Syllable-Based Sequence-to-Sequence Speech Recognition with the Transformer in Mandarin Chinese

https://github.com/geopavlakos/object3d

6-DoF Object Pose from Semantic Keypoints

https://github.com/georg-wolflein/hoechstgan

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/gerardoglz/GANs

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/gfoxx29/Image-Captioning

Show and Tell: A Neural Image Caption Generator

https://github.com/gggggeun/DeepLearning_Architecture

Attention Is All You Need

https://github.com/ghamarian/rfcn

R-FCN: Object Detection via Region-based Fully Convolutional Networks

https://github.com/giagia96/DeepComedy

Attention Is All You Need

https://github.com/gidobot/SilhoNet

SilhoNet: An RGB Method for 6D Object Pose Estimation

https://github.com/gitboyzorro5/tensor2tensor

Tensor2Tensor for Neural Machine Translation

https://github.com/gjp1203/LIV360SV

Spatial Transformer Networks

https://github.com/glados97/dsa-model

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

https://github.com/glample/fastBPE

Neural Machine Translation of Rare Words with Subword Units

https://github.com/glouppe/info8010-deep-learning

Attention Is All You Need

https://github.com/gm3g11/VAE_GAN_pytorch

Autoencoding beyond pixels using a learned similarity metric

https://github.com/gmum/dl-mo-2021

Attention Is All You Need

https://github.com/gnouhp/PyTorch-CoordConv

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/going-doer/paper2code

Attention Is All You Need

https://github.com/goldenbili/Bert_Test2

Attention Is All You Need

https://github.com/goldenbili/Bert_Test3

Attention Is All You Need

https://github.com/goldenbili/bert_lamb_pretrain

Attention Is All You Need

https://github.com/gongrennengzhi/nmt

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/gongshuangshuang/deep-text-corrector

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/goodluck4s/bert-

Attention Is All You Need

https://github.com/google-research-datasets/RxR

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

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