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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 4
- 보존된 평가 기록
- 5
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MiuGod0126/RAM-Paddle
https://github.com/Mixergi/MelGAN
MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
https://github.com/Mogbo/image-caption
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/MohamedAbdelsalam9/TT-Transformer
https://github.com/MonsterPeng/Deformable-ConvNets-master
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/Montia/bw2color
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/Moon-sung-woo/ParallelWaveGan_korean
MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
https://github.com/MorenoLaQuatra/bart-it
mT5: A massively multilingual pre-trained text-to-text transformer
https://github.com/MostafaDehghani/bAbI-T2T
https://github.com/Mrnoorsingh/image-caption
https://github.com/Muennighoff/Im2txt
Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge
https://github.com/Mugilvanan/stnbhwd
https://github.com/MultiPath/NA-NMT
https://github.com/MuradBozik/audio-style-transfer
MelGAN-VC: Voice Conversion and Audio Style Transfer on arbitrarily long samples using Spectrograms
https://github.com/N0Wire/Traffic-Sign-Detection
https://github.com/NDCHIRO/Cartoonizer
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/NSCL/yolact_instance
https://github.com/NVIDIA/DeepLearningExamples/tree/master/DGLPyTorch/DrugDiscovery/SE3Transformer
SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks
https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Translation/GNMT
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
https://github.com/NVIDIA/UnsupervisedLandmarkLearning
Unsupervised Part-Based Disentangling of Object Shape and Appearance
https://github.com/NVIDIA/sentiment-discovery
https://github.com/NVIDIAAICITYCHALLENGE/AICity_Team6_ISU
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/NVlabs/On-Learning-Sets-of-Symmetric-Elements
https://github.com/NanYi-hub/Deep-Generative-Network
Autoencoding beyond pixels using a learned similarity metric
https://github.com/NaskyD/convnet
https://github.com/NathanDeMaria/AugmentedCycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Negus25/MindSpore-StarGAN
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
https://github.com/Neoanarika/torchexplainer
https://github.com/Nero-Disney/MICROSOFT_Bringing-Old-Photos-Back-to-Life
https://github.com/NeuralVFX/lighting-swap-cyclegan
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/NeuralVFX/pix2pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/Neznakomec/ml-project-transformers
https://github.com/Nguyendat-bit/pix2pix-tensorflow
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/Nic5472K/FriendsOOGroup_TCN
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
https://github.com/NicholasKX/ShowAndTell
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.