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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/rashad101/dialokg
공식 코드DialoKG: Knowledge-Structure Aware Task-Oriented Dialogue Generation
https://github.com/raulsenaferreira/prdc_2021_sut_module
공식 코드Benchmarking Safety Monitors for Image Classifiers with Machine Learning
https://github.com/raymond-qiancx/dyncim
공식 코드DynCIM: Dynamic Curriculum for Imbalanced Multimodal Learning
https://github.com/raymondzmc/attention-pattern-exploitation
공식 코드https://github.com/razaimam45/fine-tune-distillation
공식 코드Domain Adaptable Fine-Tune Distillation Framework For Advancing Farm Surveillance
https://github.com/rbrandt1/precise-benchmarking-of-xai
공식 코드https://github.com/rcap107/retrieve-merge-predict
공식 코드https://github.com/rcap107/starmie
공식 코드https://github.com/rcap107/yadl
공식 코드https://github.com/recovercoalition/recover
공식 코드https://github.com/recsys-benchmark/daisyrec-v2.0
공식 코드DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation
https://github.com/reczoo/BARS
공식 코드BARS-CTR: Open Benchmarking for Click-Through Rate Prediction
https://github.com/regan-zhang/hadis
공식 코드Deep Clustering with Diffused Sampling and Hardness-aware Self-distillation
https://github.com/reih02/distilled_circuits
공식 코드Distilled Circuits: A Mechanistic Study of Internal Restructuring in Knowledge Distillation
https://github.com/reliable-information-lab-hevs/benchmark_llm_texts_detection
공식 코드LLM Detectors Still Fall Short of Real World: Case of LLM-Generated Short News-Like Posts
https://github.com/remaro-network/kd-yolox-vit
공식 코드Knowledge Distillation in YOLOX-ViT for Side-Scan Sonar Object Detection
https://github.com/remaro-network/rosar-framework
공식 코드ROSAR: An Adversarial Re-Training Framework for Robust Side-Scan Sonar Object Detection
https://github.com/reml-group/deliberation-on-priors
공식 코드Deliberation on Priors: Trustworthy Reasoning of Large Language Models on Knowledge Graphs
https://github.com/reml-lab/URSABench
공식 코드https://github.com/remotebiosensing/rppg
공식 코드Remote Bio-Sensing: Open Source Benchmark Framework for Fair Evaluation of rPPG
https://github.com/rendchevi/nix-tts
공식 코드Nix-TTS: Lightweight and End-to-End Text-to-Speech via Module-wise Distillation
https://github.com/renjie-liang/emet
공식 코드Efficient Temporal Sentence Grounding in Videos with Multi-Teacher Knowledge Distillation
https://github.com/researchmm/ckdn
공식 코드Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessment
https://github.com/respiratory-support/respiratory-interventions
공식 코드https://github.com/retailmarketingai/retailsynth
공식 코드RetailSynth: Synthetic Data Generation for Retail AI Systems Evaluation
https://github.com/reubenjrouse/VCap_CapsuleVision2024
공식 코드Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy
https://github.com/revdotcom/speech-datasets
공식 코드https://github.com/rezafuru/frankensplit
공식 코드https://github.com/rezashkv/diffusion_pruning
공식 코드Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models
https://github.com/rgeirhos/dataset-pruning-metrics
공식 코드Beyond neural scaling laws: beating power law scaling via data pruning
https://github.com/rgeirhos/shortcut-perspective
공식 코드https://github.com/rhe502/icml25-dpcr
공식 코드https://github.com/rhermary/plume
공식 코드Removing Geometric Bias in One-Class Anomaly Detection with Adaptive Feature Perturbation
https://github.com/ricgama/maenvs4vrp
공식 코드https://github.com/richard-guyunqi/BlenderGym-Open
공식 코드BlenderGym: Benchmarking Foundational Model Systems for Graphics Editing
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