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Few-Shot 3D Point Cloud Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/joebluems/Soul_Of_AI
https://github.com/johnsonsign/mast-pre
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
https://github.com/joongbo/tta
https://github.com/joyhsu0504/ns3d
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
https://github.com/joytianya/google_bert
https://github.com/jtpils/treegan
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/julie-tang00/point-bert
Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling
https://github.com/junhahyung/bert_finetune
https://github.com/junhahyung/bert_transfer
https://github.com/kaituoxu/Speech-Transformer
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/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/kotu931226/classifier_transformer_pytorch
https://github.com/kpot/keras-transformer
https://github.com/krantirk/BERT-Pretrained-model
https://github.com/kriesbeck/masked-language-prediction
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/leehyunjun-seoultech/seoultech
https://github.com/leejieun51/transformer
https://github.com/lehinevych/SAAN
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.