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Knowledge Distillation
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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/haoyu-r/how-to-manage-tinyml-at-scale
공식 코드How to Manage Tiny Machine Learning at Scale: An Industrial Perspective
https://github.com/haoyuzhao123/leanineqcomp
공식 코드https://github.com/happylkx/mmcode
공식 코드https://github.com/harborsarah/lircdepth
공식 코드https://github.com/harvard-edge/airlearning
공식 코드Air Learning: A Deep Reinforcement Learning Gym for Autonomous Aerial Robot Visual Navigation
https://github.com/harvardnlp/nmt-android
공식 코드https://github.com/harvardnlp/seq2seq-attn
공식 코드https://github.com/hasanssl/bdslw60_code
공식 코드https://github.com/haseebaslam95/MT-PKDOT
공식 코드Multi Teacher Privileged Knowledge Distillation for Multimodal Expression Recognition
https://github.com/haseebs/Pseudo-rehearsal-Incremental-Learning
공식 코드Distillation Techniques for Pseudo-rehearsal Based Incremental Learning
https://github.com/hasibzunair/rsud20k
공식 코드RSUD20K: A Dataset for Road Scene Understanding In Autonomous Driving
https://github.com/hathawayxxh/CRCKD
공식 코드Categorical Relation-Preserving Contrastive Knowledge Distillation for Medical Image Classification
https://github.com/havrylovv/isegprobe
공식 코드Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation
https://github.com/hawlyq/infometic
공식 코드InfoMetIC: An Informative Metric for Reference-free Image Caption Evaluation
https://github.com/hayatkhan8660-maker/DVFL-Net
공식 코드https://github.com/hbp-unibi/SNABSuite
공식 코드Benchmarking Deep Spiking Neural Networks on Neuromorphic Hardware
https://github.com/hc-guo/sns-bench-vl
공식 코드SNS-Bench-VL: Benchmarking Multimodal Large Language Models in Social Networking Services
https://github.com/hchoi71/intra-class-patch-swap
공식 코드https://github.com/hchoi71/mix-kd
공식 코드Understanding the Role of Mixup in Knowledge Distillation: An Empirical Study
https://github.com/hcmus-thesis-gulu/context-aware-summarization
공식 코드https://github.com/hdzhang-code/smallID
공식 코드https://github.com/he-tiantian/cats
공식 코드https://github.com/heal-research/operon
공식 코드Contemporary Symbolic Regression Methods and their Relative Performance
https://github.com/healthml/active-segmentation
공식 코드Less Is More: A Comparison of Active Learning Strategies for 3D Medical Image Segmentation
https://github.com/healthx-lab/biomedcoop
공식 코드BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models
https://github.com/healthx-lab/deepsegthoracic
공식 코드https://github.com/healthylaife/fairalignmentllm
공식 코드https://github.com/heathcliff-saku/viewfool_
공식 코드ViewFool: Evaluating the Robustness of Visual Recognition to Adversarial Viewpoints
https://github.com/heaventian93/ImageNet-Models-on-Domain-Adaptation
공식 코드https://github.com/heejkoo9/nechov2
공식 코드https://github.com/heidelberg-nlp/ancient-language-models
공식 코드https://github.com/heidelberg-nlp/cc-shap-vlm
공식 코드https://github.com/heitorcfelix/singleshot6Dpose
공식 코드Squeezed Deep 6DoF Object Detection Using Knowledge Distillation
https://github.com/hellohaptik/HINT3
공식 코드https://github.com/helloworldlty/muse-gnn
공식 코드MuSe-GNN: Learning Unified Gene Representation From Multimodal Biological Graph Data
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.