Papers with Code 아카이브
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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
- 5
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ChiZhangRIT/BNRHN
Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge
https://github.com/Chinnu1103/Machine-Translation-using-Transformers
https://github.com/Chloejay/image_caption_app
Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models
https://github.com/Chonwai/Learning_BERT
https://github.com/Chung-I/Variational-Recurrent-Autoencoder-Tensorflow
Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
https://github.com/Cirhuzalain/cdsmodel
https://github.com/Code-0x00/caffe_windows
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/Colanim/BERT_STS-B
https://github.com/CompVis/unsupervised-part-segmentation
Unsupervised Part-Based Disentangling of Object Shape and Appearance
https://github.com/CongBao/ChatBot
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
https://github.com/CoryRJ/cycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/CreativeCodingLab/DeepIllumination
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/DC1991/FS_Net
https://github.com/DCYN/Ramdomized-Clinical-Trail-Classification
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/DJJune/Transformer
https://github.com/DLHacks/pix2pix_PAN
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/DSKSD/RNN-for-Joint-NLU
Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling
https://github.com/Daeijavad/MAD-GAN
https://github.com/DanieleVeri/deep_comedy
https://github.com/Danyache/skoltech_image_cap
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/DarkGeekMS/Tensorflow-GANs-Architectures-Implementation
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Data-drone/cvnd_image_captioning
https://github.com/DataScienceNigeria/Synesthetic-Variational-Autoencoder-SynVAE-
https://github.com/DataXujing/yolact_pytorch
https://github.com/Dave870907/Transformer
https://github.com/DavidCastilloAlvarado/U-NET_AUTOENCODER
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/DavidHarar/Siamese-Networks-for-name-nickname-similarity
https://github.com/DeadAt0m/adafactor-pytorch
Adafactor: Adaptive Learning Rates with Sublinear Memory Cost
https://github.com/Deeksha96/Im2Latex
https://github.com/DeepGraphLearning/CGCF-ConfGen
Learning Gradient Fields for Molecular Conformation Generation
https://github.com/DeepRNN/image_captioning
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/DeepikaKaranji/CycleGAN-Unpaired-Image-translation
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/DeligientSloth/QQsim
https://github.com/DeligientSloth/bert-tensorflow
https://github.com/DeokO/bert-excercise-ongoing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.