https://github.com/mdcnn/mdcnn.github.io
Deep Multiple Description Coding by Learning Scalar Quantization
Papers with Code 아카이브
NLP based Person Retrival
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/meghu2791/DeepLearningModels
Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond
https://github.com/mehdibenamorr/Token-Positional-Bias
https://github.com/mehdibenamorr/bert-positional-bias-ner
https://github.com/meizi1114/bert
https://github.com/memmelma/PrototypeDL-pytorch
https://github.com/metavoiceio/metavoice-src
https://github.com/mf1832146/tree-transformer
Improving Automatic Source Code Summarization via Deep Reinforcement Learning
https://github.com/mf1832146/tree_transformer_2.0
Improving Automatic Source Code Summarization via Deep Reinforcement Learning
https://github.com/mfarhadi98/Use-Capsule-Networks-for-kddcup
https://github.com/mhamedLmarbouh/Attention-is-all-you-need
https://github.com/mhaseeb123/DeepMSim
https://github.com/michael-wzhu/mpnet_zh
MASS: Masked Sequence to Sequence Pre-training for Language Generation
https://github.com/michaelfarrell76/End-To-End-Generative-Dialogue
Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models
https://github.com/microcoder-py/attn-is-all-you-need
https://github.com/microsoft/BlingFire
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
https://github.com/microsoft/MPNet
MASS: Masked Sequence to Sequence Pre-training for Language Generation
https://github.com/microsoft/Table-Pretraining
https://github.com/microsoft/deepnmt
https://github.com/microsoft/dialoglm
UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training
https://github.com/microsoft/fastseq
https://github.com/microsoft/onnxruntime-training-examples
https://github.com/microsoft/protein-sequence-models
https://github.com/microsoft/qlib/tree/main/examples/benchmarks/ADD
ADD: Augmented Disentanglement Distillation Framework for Improving Stock Trend Forecasting
https://github.com/midobal/OpenNMT-py
https://github.com/mike-a-yen/date-translation
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/mike-n-7/ADEM
A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues
https://github.com/milesial/Pytorch-UNet
U-Net v2: Rethinking the Skip Connections of U-Net for Medical Image Segmentation
https://github.com/mim-solutions/bert_for_longer_texts
https://github.com/mim-solutions/roberta_for_longer_texts
https://github.com/mindee/doctr
Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition
https://github.com/mindflow-institue/ssct
Unlocking Fine-Grained Details with Wavelet-based High-Frequency Enhancement in Transformers
https://github.com/mindslab-ai/hififace
HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swapping
https://github.com/mindspore-ai/contrib/tree/master/intern/Knowing-When-to-Look-Adaptive-Attention
Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
https://github.com/mindspore-ai/contrib/tree/master/papers/UColor
Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding
https://github.com/mindspore-ai/models/blob/master/official/cv/deeplabv3plus
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.