Papers with Code 아카이브

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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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코드

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

https://github.com/xiaohu2015/pvt_detectron2

Conditional Positional Encodings for Vision Transformers

https://github.com/xiaopingzhong/bert-finetune-for-classfier

Attention Is All You Need

https://github.com/xiaopp123/bert_explain

Attention Is All You Need

https://github.com/xiaoxu1025/r-fcn

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

https://github.com/xiaoyongzhu/Deformable-ConvNets

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

https://github.com/xiaoyuin/tntspa

Neural Machine Translating from Natural Language to SPARQL

https://github.com/xieyxclack/factual_coco

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

https://github.com/xingchenzhao/Generating-Human-Skeletons-with-Mutual-Actions-WGAN-Pytorch

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/xingchenzhao/deep-learning-team-project

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/xingjian-zhang/massw

Attention Is All You Need

https://github.com/xingpingdong/CLNet-tracking

SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks

https://github.com/xinykou/Against_Jailbreak

A Unified Strategy for Multilingual Grammatical Error Correction with Pre-trained Cross-Lingual Language Model

https://github.com/xitianxiaofeixue/BERT

Attention Is All You Need

https://github.com/xiuyu0000/papers_with_examples/tree/main/cyclegan

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

https://github.com/xiuyu0000/papers_with_examples/tree/main/pix2pix

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/xmu-xiaoma666/External-Attention-pytorch

Attention Is All You Need

https://github.com/xrick/PyTorch_Transformer

Weighted Transformer Network for Machine Translation

https://github.com/xslidi/EfficientNets_ddl_apex

GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

https://github.com/xuanlinli17/autoregressive_inference

Sequence-Level Knowledge Distillation

https://github.com/xuewyang/Fashion_Captioning

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

https://github.com/xumengyaamy/swinmlp_trancap

Surgical Instruction Generation with Transformers

https://github.com/xuzhezhaozhao/bert_reading

Attention Is All You Need

https://github.com/xw-hu/Mask-ShadowGAN

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

https://github.com/xxlya/CycleGAN

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

https://github.com/xycforgithub/Airbnb-Comparison

Multi-task Learning with Sample Re-weighting for Machine Reading Comprehension

https://github.com/xypu98/two-stage-HDR-video

A Two-stage Deep Network for High Dynamic Range Image Reconstruction

https://github.com/yakazimir/zubr_public

Function Assistant: A Tool for NL Querying of APIs

https://github.com/yakhyo/EfficientNet-PyTorch

GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

https://github.com/yale-lily/ehrkit-2022

Ascle: A Python Natural Language Processing Toolkit for Medical Text Generation

https://github.com/yangbang18/clfm

ZeroNLG: Aligning and Autoencoding Domains for Zero-Shot Multimodal and Multilingual Natural Language Generation

https://github.com/yanggeng1995/FB-MelGAN

MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis

https://github.com/yanggeng1995/GAN-TTS

MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis

https://github.com/yangletliu/finlora

BERTScore: Evaluating Text Generation with BERT

https://github.com/yangxuntu/vrd

Visual Translation Embedding Network for Visual Relation Detection

https://github.com/yangyucheng000/CycleGan

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

https://github.com/yangyucheng000/MSpaper/tree/main/U-GAT-IT

Feature Quantization Improves GAN Training

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