https://github.com/moein-shariatnia/OpenAI-CLIP
Learning Transferable Visual Models From Natural Language Supervision
Papers with Code 아카이브
Knowledge Distillation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 평가표
- 11
- 보존된 평가 기록
- 90
Knowledge distillation is the process of transferring knowledge from a large model to a smaller one. While large models (such as very deep neural networks or ensembles of many models) have higher knowledge capacity than small models, this capacity might not be fully utilized.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/monologg/distilkobert
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/mr-eggplant/eata
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
https://github.com/mr-eggplant/sar
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
https://github.com/mroberto166/nio
OpenFWI: Large-Scale Multi-Structural Benchmark Datasets for Seismic Full Waveform Inversion
https://github.com/msd-irimas/multi_comparison_matrix
Convolutional and Deep Learning based techniques for Time Series Ordinal Classification
https://github.com/msorkhpar/wiki-entity-summarization-preprocessor
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/mtanti/coco-caption
https://github.com/mtcld/mmdetection
https://github.com/mtresearcher/FastSpeech2
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/muktac5/ciir-liverag
https://github.com/munjalbharti/Knowledge-Distillation-for-End-to-End-Person-Search
https://github.com/muzairkhattak/multimodal-prompt-learning
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/n1kkqt/line-art-colorization
User-Guided Deep Anime Line Art Colorization with Conditional Adversarial Networks
https://github.com/naamiinepal/xrayto3d-benchmark
Benchmarking Encoder-Decoder Architectures for Biplanar X-ray to 3D Shape Reconstruction
https://github.com/nageshsinghc4/deepwrap
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/nahidalam/open_clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/namdvt/Focal-loss-pytorch-implementation
https://github.com/nandomp/AICollaboratory
The Arcade Learning Environment: An Evaluation Platform for General Agents
https://github.com/nasir6/detection
https://github.com/naver-ai/rethinking-proxy-reward
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
https://github.com/nayanr/instance_segmentation
https://github.com/ncbi-nlp/ML_Net
ML-Net: multi-label classification of biomedical texts with deep neural networks
https://github.com/ncbi/BioRED
https://github.com/ndkgit339/fastspeech2-filled_pause_speech_synthesis
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/nemonameless/mmkp
https://github.com/neshitov/UNet
https://github.com/neurai-lab/dsp
Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations
https://github.com/neurosim/DNN_NeuroSim_V2.1
https://github.com/new-okaerinasai/mmdetection
https://github.com/nf-core/methylseq
https://github.com/ngonhi/mmdetection
https://github.com/nguoido/Yolo-alexeyAB
https://github.com/nicola-bastianello/optopy
https://github.com/nihui/realsr-ncnn-vulkan
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
https://github.com/nikhilrayaprolu/food-version2
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.