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/Tao-Mind/QDD_Net

QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension

https://github.com/Taoudi/Monet-ization-of-Images

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

https://github.com/TellinaTool/nl2bash

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/TencentYoutuResearch/SelfSupervisedLearning-DSM

Spatial Transformer Networks

https://github.com/TensorSpeech/TensorFlowASR

Sequence Transduction with Recurrent Neural Networks

https://github.com/TensorSpeech/TensorflowTTS

MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis

https://github.com/ThAIKeras/bert

Neural Machine Translation of Rare Words with Subword Units

https://github.com/The-AI-Summer/self-attention-cv

Self-Attention with Relative Position Representations

https://github.com/The-AI-Summer/self_attention

Attention Is All You Need

https://github.com/TheInfamousWayne/CheXNet

Learning to diagnose from scratch by exploiting dependencies among labels

https://github.com/TheTensorDude/transformer-tf

Attention Is All You Need

https://github.com/TheZino/Image_captioning_SATell

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

https://github.com/ThibaultGROUEIX/3D-CODED

Learning elementary structures for 3D shape generation and matching

https://github.com/ThisuriLekamge/Stock-Price-Prediction-on-Bitcoin-trading-data-using-LSTM-with-PyTorch

Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks

https://github.com/Tiagoblima/EA-Translator

Evolutionary Algorithm for Sinhala to English Translation

https://github.com/Tian-Jiang/ModularGAN

Modular Generative Adversarial Networks

https://github.com/TieDanCuihua/transformer-Attention-is-All-You-Need

Attention Is All You Need

https://github.com/Tikquuss/meta_XLM

Cross-lingual Language Model Pretraining

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

Show and Tell: A Neural Image Caption Generator

https://github.com/TimVerion/caffe_rfcn

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

https://github.com/Tony-Y/pytorch_warmup

On the Variance of the Adaptive Learning Rate and Beyond

https://github.com/Tony4469/laser-agir

Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings

https://github.com/TonyX19/bert_hyperpartisan

Attention Is All You Need

https://github.com/TooTouch/3D_CoordConv_Segmentation

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/Trustworthy-AI-Group/TransferAttack

Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks

https://github.com/UH-SERG/SIVAND

A Convolutional Attention Network for Extreme Summarization of Source Code

https://github.com/UKPLab/EasyNMT

Multilingual Translation with Extensible Multilingual Pretraining and Finetuning

https://github.com/UMich-BipedLab/AprilTag_ROS

Improvements to Target-Based 3D LiDAR to Camera Calibration

https://github.com/UMich-BipedLab/ROB101-ExtrinsicCalibrationProblem

Improvements to Target-Based 3D LiDAR to Camera Calibration

https://github.com/UMich-BipedLab/automatic_lidar_camera_calibration

Improvements to Target-Based 3D LiDAR to Camera Calibration

https://github.com/UMich-BipedLab/lidar_simulator

Improvements to Target-Based 3D LiDAR to Camera Calibration

https://github.com/UMich-BipedLab/sync_lidartag_apriltag

Improvements to Target-Based 3D LiDAR to Camera Calibration

https://github.com/UdbhavPrasad072300/Transformer-Implementation

Attention Is All You Need

https://github.com/UdbhavPrasad072300/Transformer-Implementation-and-Language-Translation

Attention Is All You Need

https://github.com/UdbhavPrasad072300/Transformer-Implementations

Attention Is All You Need

https://github.com/UdonDa/StarGAN-v2-pytorch-nonofficial

StarGAN v2: Diverse Image Synthesis for Multiple Domains

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