https://github.com/fengjiang5/knowledge-distillation-from-cylinder3d-to-polarnet
공식 코드Knowledge Distillation from 3D to Bird's-Eye-View for LiDAR Semantic Segmentation
Papers with Code 아카이브
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/fermint/visualsearchbenchmark
공식 코드https://github.com/ffhibnese/model-inversion-attack-toolbox
공식 코드MIBench: A Comprehensive Framework for Benchmarking Model Inversion Attack and Defense
https://github.com/fhgr/careercoach2022
공식 코드Slot Filling for Extracting Reskilling and Upskilling Options from the Web
https://github.com/figlab/imuposer
공식 코드IMUPoser: Full-Body Pose Estimation using IMUs in Phones, Watches, and Earbuds
https://github.com/finmu/ml-esg_codes
공식 코드https://github.com/fitushar/AI-in-Lung-Health-Benchmarking-Detection-and-Diagnostic-Models-Across-Multiple-CT-Scan-Datasets
공식 코드AI in Lung Health: Benchmarking Detection and Diagnostic Models Across Multiple CT Scan Datasets
https://github.com/fitzpchao/hicd
공식 코드HiCD: Change Detection in Quality-Varied Images via Hierarchical Correlation Distillation
https://github.com/flagopen/flagembedding
공식 코드https://github.com/flanders-make-vzw/hpc2edge
공식 코드Optimizing edge AI models on HPC systems with the edge in the loop
https://github.com/flipz357/schroedingersevaluation
공식 코드Schroedinger's Threshold: When the AUC doesn't predict Accuracy
https://github.com/flux661/Capsule-Vision-2024-Solution-Team_Layer_Players
공식 코드Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy
https://github.com/fmusio/topcowsubmissions
공식 코드https://github.com/folivetti/tir
공식 코드Transformation-Interaction-Rational Representation for Symbolic Regression
https://github.com/forjiuzhou/Spherical-Knowledge-Distillation
공식 코드Reducing the Teacher-Student Gap via Spherical Knowledge Disitllation
https://github.com/fossr-project/ontogenia-cini
공식 코드Bench4KE: Benchmarking Automated Competency Question Generation
https://github.com/foundation-multimodal-models/capture
공식 코드https://github.com/fpcsong/BiLD
공식 코드BiLD: Bi-directional Logits Difference Loss for Large Language Model Distillation
https://github.com/fpsluozi/tofindwaldo
공식 코드To Find Waldo You Need Contextual Cues: Debiasing Who's Waldo
https://github.com/fqhank/align-kd
공식 코드Align-KD: Distilling Cross-Modal Alignment Knowledge for Mobile Vision-Language Model
https://github.com/fqixiang/patch_llm_benchmarking_with_psychometrics
공식 코드https://github.com/fra31/auto-attack
공식 코드https://github.com/frakkerman/ooh_code
공식 코드Learning Dynamic Selection and Pricing of Out-of-Home Deliveries
https://github.com/francois-rd/accord
공식 코드$\texttt{ACCORD}$: Closing the Commonsense Measurability Gap
https://github.com/franziska-schirrmacher/lpr-uncertainty
공식 코드Benchmarking Probabilistic Deep Learning Methods for License Plate Recognition
https://github.com/fraunhoferivi/ssp
공식 코드https://github.com/frederikkemarin/bend
공식 코드BEND: Benchmarking DNA Language Models on biologically meaningful tasks
https://github.com/freeformrobotics/progressive-self-distillation-for-ground-to-aerial-perception-knowledge-transfer
공식 코드Progressive Self-Distillation for Ground-to-Aerial Perception Knowledge Transfer
https://github.com/fremigereau/mtda_kd_reid
공식 코드Knowledge Distillation for Multi-Target Domain Adaptation in Real-Time Person Re-Identification
https://github.com/froot-netsys/netpress
공식 코드NetPress: Dynamically Generated LLM Benchmarks for Network Applications
https://github.com/fscdc/awesome-efficient-reasoning-models
공식 코드https://github.com/fschmid56/efficientat
공식 코드Efficient Large-scale Audio Tagging via Transformer-to-CNN Knowledge Distillation
https://github.com/fschmid56/pretrainedsed
공식 코드Effective Pre-Training of Audio Transformers for Sound Event Detection
https://github.com/fsoft-aic/waver
공식 코드https://github.com/fsschneider/deepobs
공식 코드https://github.com/fszatkowski/cl-teacher-adaptation
공식 코드Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-free Continual Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.