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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/demelin/nematode
https://github.com/denizyuret/fastsubs-googlecode
https://github.com/derleeG/EAPPnP
MLPnP - A Real-Time Maximum Likelihood Solution to the Perspective-n-Point Problem
https://github.com/derylucio/Transformer
https://github.com/destinyls/SMOKE
SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation
https://github.com/deterministic-algorithms-lab/Large-XLM
https://github.com/devonds/automated_data_vis
https://github.com/dewanderelex/LanguageTranslation
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
https://github.com/deworrall92/harmonicConvolutions
Harmonic Networks: Deep Translation and Rotation Equivariance
https://github.com/dg-enlens/banet-depth-prediction
Bidirectional Attention Network for Monocular Depth Estimation
https://github.com/dgedon/CycleGAN-for-Emojis
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/dgonzalez-ri/neural-visual-storyteller
Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge
https://github.com/dguo98/seqmix
https://github.com/dhassault/cyclegan_pytorch
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/dhatchi711/espnet-emotional-news
Reproducing Whisper-Style Training Using an Open-Source Toolkit and Publicly Available Data
https://github.com/dhecloud/simple_language_modelling
https://github.com/dhiraa/tener
https://github.com/dhruvdcoder/sparse-structured-attention
A Regularized Framework for Sparse and Structured Neural Attention
https://github.com/dianaglzrico/neural-visual-storyteller
Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge
https://github.com/dice-group/KG-NMT
https://github.com/dicksiano/Spatial-Transformer-Network
https://github.com/dipjyoti92/StarGAN-Voice-Conversion
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
https://github.com/dipjyoti92/StarGAN-Voice-Conversion-2
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
https://github.com/distractor-generation/dg_survey
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/divyachandana/yolact
https://github.com/djain454/Show-Attend-and-Tell-Neural-Image-Caption-Generation-with-Visual-Attention
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/dksifoua/Neural-Machine-Translation
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
https://github.com/dl-nlp/dl-nlp.github.io
https://github.com/dmdomo/AttentionGAN
Attention-Guided Generative Adversarial Networks for Unsupervised Image-to-Image Translation
https://github.com/dmeoli/neuro-sat
From Gameplay to Symbolic Reasoning: Learning SAT Solver Heuristics in the Style of Alpha(Go) Zero
https://github.com/dmlc/dgl/tree/master/examples/pytorch/transformer
BP-Transformer: Modelling Long-Range Context via Binary Partitioning
https://github.com/dmlc/gluon-nlp
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
https://github.com/doctranslate-io/viet-translation-llm
https://github.com/dongdong199408/teachchatrobot
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/dr-costas/clotho-baseline-dataset
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.