Papers with Code 아카이브
NLP based Person Retrival
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mindspore-ai/models/blob/master/research/cv/AttGAN
AttGAN: Facial Attribute Editing by Only Changing What You Want
https://github.com/mindspore-ai/models/blob/master/research/cv/deeplabv3plus
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
https://github.com/mindspore-ai/models/blob/master/research/cv/mobilenetv3_large
https://github.com/mindspore-ai/models/tree/master/official/nlp/gnmt_v2
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
https://github.com/mindspore-ai/models/tree/master/official/nlp/gru
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
https://github.com/mindspore-ai/models/tree/master/official/nlp/mass
MASS: Masked Sequence to Sequence Pre-training for Language Generation
https://github.com/mindspore-ai/models/tree/master/official/nlp/transformer
https://github.com/mindspore-ai/models/tree/master/research/cv/Neighbor2Neighbor
UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation
https://github.com/mindspore-ai/models/tree/master/research/cv/OCRNet
Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation
https://github.com/mindspore-ai/models/tree/master/research/cv/STGAN
STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing
https://github.com/mindspore-ai/models/tree/master/research/mm/opt
OPT: Omni-Perception Pre-Trainer for Cross-Modal Understanding and Generation
https://github.com/mindspore-courses/Deep-Tutorials-for-MindSpore
https://github.com/mindspore-courses/DeepNLP-models-MindSpore
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/attention/SelfAttention.py
https://github.com/mindspore-courses/heads-on-mindspore/tree/main/6-speech_recognition
Joint CTC-Attention based End-to-End Speech Recognition using Multi-task Learning
https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/mobilenet_v3.py
https://github.com/mindspore-lab/mindocr
MASTER: Multi-Aspect Non-local Network for Scene Text Recognition
https://github.com/minerva-ml/open-solution-mapping-challenge
TernausNetV2: Fully Convolutional Network for Instance Segmentation
https://github.com/ming024/FastSpeech2
https://github.com/mingkaid/rl-prompt
TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted Restyling
https://github.com/mingyue-cheng/crosstimenet
Diffusion Auto-regressive Transformer for Effective Self-supervised Time Series Forecasting
https://github.com/mingyue-cheng/timemae
Diffusion Auto-regressive Transformer for Effective Self-supervised Time Series Forecasting
https://github.com/mingyuyng/OFDM-guided-JSCC
Deep Joint Source Channel Coding for WirelessImage Transmission with OFDM
https://github.com/minhpqn/vietner
Attentive Neural Network for Named Entity Recognition in Vietnamese
https://github.com/miniii222/Graduate-Paper
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
https://github.com/minus31/Portrait-segmentation
https://github.com/miroblog/AdaptiveAttention
Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
https://github.com/mirthai/csa-net
TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
https://github.com/mistasse/modulom-panopticdeeplab
Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation
https://github.com/mistralai/mistral-src
https://github.com/mithunpaul08/bert_tensorflow
https://github.com/mitran27/Attention-is-all-you-Need
https://github.com/mitscha/dplc
https://github.com/mitvis/vistext
https://github.com/mkara44/transunet_pytorch
TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.