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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/HERMINDERSINGH1234/ML_Extra_Resolution_Increases

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

https://github.com/HHousen/TransformerSum

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

https://github.com/HIT-SCIR/ELMoForManyLangs

On Using Very Large Target Vocabulary for Neural Machine Translation

https://github.com/HKUST-KnowComp/R-Net

Attention Is All You Need

https://github.com/HUSTSYJ/DA_dahazing

Domain Adaptation for Image Dehazing

https://github.com/Ha0Tang/HandGestureRecognition

Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation

https://github.com/Ha0Tang/LocalGlobalGAN

Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation

https://github.com/Haggaim/On-Learning-Sets-of-Symmetric-Elements

On Learning Sets of Symmetric Elements

https://github.com/HamadYA/GhostFaceNets

Lookahead Optimizer: k steps forward, 1 step back

https://github.com/Han-JD/GRU-D

Regularizing and Optimizing LSTM Language Models

https://github.com/HanNight/RE-T5

VATEX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research

https://github.com/HanhaiNotHai/transformer

Attention Is All You Need

https://github.com/Hanlard/M2M-by-fastmoe

Beyond English-Centric Multilingual Machine Translation

https://github.com/HaoXu-Work/FINet

FINet: Dual Branches Feature Interaction for Partial-to-Partial Point Cloud Registration

https://github.com/Harry-Muzart/harry-muzart.github.io

Attention Is All You Need

https://github.com/HarshKhandelwal1552/Language_classifier_with_Naive_Bayes

Neural Machine Translation of Rare Words with Subword Units

https://github.com/HarunoriKawano/Conformer

Self-Attention with Relative Position Representations

https://github.com/HastingsGreer/InverseConsistency

ICON: Learning Regular Maps Through Inverse Consistency

https://github.com/HawkAaron/warp-transducer

Sequence Transduction with Recurrent Neural Networks

https://github.com/HazemLahoual/Project-Machine-Learning

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

https://github.com/HelioStrike/Spatial-Transformation-Network

Spatial Transformer Networks

https://github.com/Helsinki-NLP/OpenNMT-py

Convolutional Sequence to Sequence Learning

https://github.com/Helsinki-NLP/shared-info

Attention Is All You Need

https://github.com/HemaDevaSagar35/NeuralMachineTranslation-French2English

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/HendrikStrobelt/Seq2Seq-Vis

Seq2Seq-Vis: A Visual Debugging Tool for Sequence-to-Sequence Models

https://github.com/Henry-E/OpenNMT-py

OpenNMT: Neural Machine Translation Toolkit

https://github.com/HeshamElAbd/SelfAttentionLangModel

Attention Is All You Need

https://github.com/Hoang-it/orhcid_classification

YOLACT: Real-time Instance Segmentation

https://github.com/HrishavBakulBarua/Cycle-HDR

LLM-HDR: Bridging LLM-based Perception and Self-Supervision for Unpaired LDR-to-HDR Image Reconstruction

https://github.com/HsinYingLee/MDMM

Diverse Image-to-Image Translation via Disentangled Representations

https://github.com/HughKu/Im2txt

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

https://github.com/HyelinNAM/ContrastiveDenoisingScore

Contrastive Denoising Score for Text-guided Latent Diffusion Image Editing

https://github.com/HzcIrving/DeepLearning_PlayGround

Attention Is All You Need

https://github.com/IBM/MAX-Spatial-Transformer-Network

Spatial Transformer Networks

https://github.com/IBM/pytorch-seq2seq

Convolutional Sequence to Sequence Learning

https://github.com/ICSEG/M2TS

A parallel corpus of Python functions and documentation strings for automated code documentation and code generation

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