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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/acecreamu/angularGAN

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/acht7111020/DSMAP

Domain-Specific Mappings for Generative Adversarial Style Transfer

https://github.com/adamd1985/Temporal_Convolution_Networks_for_Energy_Demand_Forecast

Deep Learning for Time Series Forecasting: The Electric Load Case

https://github.com/adbobes/VideoSuperResolution

Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation

https://github.com/adelnabli/acid

Stochastic Gradient Push for Distributed Deep Learning

https://github.com/adi2103/AML-CoVe

Attention Is All You Need

https://github.com/aditiasthana1004/StarGAN

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

https://github.com/adityaagrawal7/speech-to-text-wavenet

Neural Machine Translation in Linear Time

https://github.com/adityarc19/traffic-vehicles-instance-segmentation

YOLACT: Real-time Instance Segmentation

https://github.com/adityarc19/yolact-plus

YOLACT: Real-time Instance Segmentation

https://github.com/adldotori/pSp

Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation

https://github.com/adnanalam53/cycleGAN

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

https://github.com/adrienchaton/BERGAN

Autoencoding beyond pixels using a learned similarity metric

https://github.com/adropulic/hls4ml-conifer

Fast inference of Boosted Decision Trees in FPGAs for particle physics

https://github.com/adropulic/hls_bdt_project

Fast inference of Boosted Decision Trees in FPGAs for particle physics

https://github.com/advaithsrao/Chatbot_Seq2seq_model

Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

https://github.com/advaitrane/GANimorph_pytorch

Improving Shape Deformation in Unsupervised Image-to-Image Translation

https://github.com/af-ai-center/bert

Attention Is All You Need

https://github.com/af-ai-center/nerblackbox

Attention Is All You Need

https://github.com/affinelayer/Pix2Pix-tensorflow

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/ag169/Image-Captioning

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

https://github.com/ag17sep/Image-Captioning-Project

Show and Tell: A Neural Image Caption Generator

https://github.com/agarwa65/lidar_camera_calibration

LiDAR-Camera Calibration using 3D-3D Point correspondences

https://github.com/agentdr1/la_mil

Attention Is All You Need

https://github.com/ahclab/naist-simulst

Attention as a Guide for Simultaneous Speech Translation

https://github.com/ahmadelsallab/lidargan

LiDAR Sensor modeling and Data augmentation with GANs for Autonomous driving

https://github.com/ahmedbesbes/cycle-gan

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

https://github.com/ahmedsalahacc/Image-Style-Transfer-with-Cycle-GAN

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

https://github.com/ahmetumutdurmus/awd-lstm

Attention Is All You Need

https://github.com/ahmetumutdurmus/zaremba

Recurrent Neural Network Regularization

https://github.com/ahsanabbas123/Transformer

Attention Is All You Need

https://github.com/ai-med/QuickNATv2

QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy

https://github.com/ai-med/quickNAT_pytorch

QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy

https://github.com/ai4bharat/indicbert

No Language Left Behind: Scaling Human-Centered Machine Translation

https://github.com/aigc-audio/audiogpt

Fast-MD: Fast Multi-Decoder End-to-End Speech Translation with Non-Autoregressive Hidden Intermediates

https://github.com/aigitsaj13/pix2pix-model

Image-to-Image Translation with Conditional Adversarial Networks

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