Papers with Code 아카이브
3D Point Cloud Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 4
- 평가표
- 5
- 보존된 평가 기록
- 216
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/kaituoxu/SpeechTransformer
https://github.com/karenacorn99/explore-bert
https://github.com/karpathy/makemore
https://github.com/karpathy/minGPT
https://github.com/karta282950/bert-multiclass
https://github.com/keiyamashita111/aaproject
https://github.com/kelly2016/multi-label-bert
https://github.com/keonlee9420/Parallel-Tacotron2
https://github.com/kh-kim/simple-nmt
https://github.com/khwajawisal/Attention-augmented-Convolutional-Neural-Networks
https://github.com/kietngt00/hmdb51-recognition
https://github.com/kiko441500/google_bert
https://github.com/kingcheng2000/bert
https://github.com/knotgrass/attention/blob/main/attn/attention.py
https://github.com/kolloldas/torchnlp
https://github.com/koritsky/pointconv
https://github.com/kotu931226/classifier_transformer_pytorch
https://github.com/kpot/keras-transformer
https://github.com/krantirk/BERT-Pretrained-model
https://github.com/krasserm/perceiver-io
https://github.com/kriesbeck/masked-language-prediction
https://github.com/krips89/mlh_mvcnn
Learning 3D Shapes as Multi-Layered Height-maps using 2D Convolutional Networks
https://github.com/krocki/np-transformer
https://github.com/ky1994/SpeechRecognition
https://github.com/labmlai/annotated_deep_learning_paper_implementations
https://github.com/lanlan96/3drm
https://github.com/ldkong1205/Robo3D
KPConv: Flexible and Deformable Convolution for Point Clouds
https://github.com/leehyunjun-seoultech/seoultech
https://github.com/leejieun51/transformer
https://github.com/lehinevych/SAAN
https://github.com/lehoanganh298/BERT-Question-Answering
https://github.com/lenacabrera/gb_mnmt
https://github.com/lennonzurich/lalala
https://github.com/lidongliang666/cv_deep_learning
https://github.com/lif31up/attention-is-all-you-need
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.