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Knowledge Distillation

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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/luiscruz/slr-green-ai

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A Systematic Review of Green AI

https://github.com/luisdavid64/Skelite

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Skelite: Compact Neural Networks for Efficient Iterative Skeletonization

https://github.com/luisfelipewb/RL4WasteCapture

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Evaluating Robustness of Deep Reinforcement Learning for Autonomous Surface Vehicle Control in Field Tests

https://github.com/luiszeni/Boosted-OICR

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Distilling Knowledge from Refinement in Multiple Instance Detection Networks

https://github.com/lujiarui/str2str

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Str2Str: A Score-based Framework for Zero-shot Protein Conformation Sampling

https://github.com/luka-kovacevic/causalregnet

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Simulation-based Benchmarking for Causal Structure Learning in Gene Perturbation Experiments

https://github.com/lukasstruppek/robust_training_on_poisoned_samples

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Leveraging Diffusion-Based Image Variations for Robust Training on Poisoned Data

https://github.com/luluppang/bcu

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Backdoor Cleansing with Unlabeled Data

https://github.com/luomancs/remuq

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End-to-end Knowledge Retrieval with Multi-modal Queries

https://github.com/luoshiya/csd

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Customizing Synthetic Data for Data-Free Student Learning

https://github.com/lutingwang/head

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HEAD: HEtero-Assists Distillation for Heterogeneous Object Detectors

https://github.com/luxiya01/mbes-registration-data

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Benchmarking Classical and Learning-Based Multibeam Point Cloud Registration

https://github.com/lvyiwei1/StylePTB

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StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer

https://github.com/lx709/reobench

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REOBench: Benchmarking Robustness of Earth Observation Foundation Models

https://github.com/lxa9867/imagefolder

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Robust Latent Matters: Boosting Image Generation with Sampling Error

https://github.com/lxa9867/r2bench

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$\text{R}^2$-Bench: Benchmarking the Robustness of Referring Perception Models under Perturbations

https://github.com/lychenyoko/content-aware-gan-compression

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Content-Aware GAN Compression

https://github.com/lyf1212/color-turbo

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Language-based Image Colorization: A Benchmark and Beyond

https://github.com/lyfeng001/mlkd

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Mutual-Learning Knowledge Distillation for Nighttime UAV Tracking

https://github.com/lzfelix/lio

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Enhancing Hyper-To-Real Space Projections Through Euclidean Norm Meta-Heuristic Optimization

https://github.com/m-muaz/Cogmen_SLT

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Bridging Modalities: Knowledge Distillation and Masked Training for Translating Multi-Modal Emotion Recognition to Uni-Modal, Speech-Only Emotion Recognition

https://github.com/m-parchami/goodteachersexplain

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Good Teachers Explain: Explanation-Enhanced Knowledge Distillation

https://github.com/m-serra/action-inference-for-video-prediction-benchmarking

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Action-conditioned Benchmarking of Robotic Video Prediction Models: a Comparative Study

https://github.com/m3rg-iitd/aila

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Autonomous Microscopy Experiments through Large Language Model Agents

https://github.com/maartenbuyl/rankformer

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RankFormer: Listwise Learning-to-Rank Using Listwide Labels

https://github.com/mac-automl/abstract-reason-benchmark

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Benchmarking Abstract and Reasoning Abilities Through A Theoretical Perspective

https://github.com/macarbonneau/MILSurvey

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Multiple Instance Learning: A Survey of Problem Characteristics and Applications

https://github.com/machanic/SimulatorAttack

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Simulating Unknown Target Models for Query-Efficient Black-box Attacks

https://github.com/machinelearning4health/medlane

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Benchmarking Automated Clinical Language Simplification: Dataset, Algorithm, and Evaluation

https://github.com/madakkmi/llpowershap

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LLpowershap: Logistic Loss-based Automated Shapley Values Feature Selection Method

https://github.com/madanela/smaller3d

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Smaller3d: Smaller Models for 3D Semantic Segmentation Using Minkowski Engine and Knowledge Distillation Methods

https://github.com/madeagents/hammerbench

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HammerBench: Fine-Grained Function-Calling Evaluation in Real Mobile Device Scenarios

https://github.com/maenzhier/grecx

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GRecX: An Efficient and Unified Benchmark for GNN-based Recommendation

https://github.com/magamba/linear-regions

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Are All Linear Regions Created Equal?

https://github.com/magantoine/jobskape

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JOBSKAPE: A Framework for Generating Synthetic Job Postings to Enhance Skill Matching

https://github.com/magics-lab/outeffhop

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Outlier-Efficient Hopfield Layers for Large Transformer-Based Models

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.