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.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Tactile MNIST: Benchmarking Active Tactile Perception
How to Backdoor Image Knowledge Distillation
B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data
SenseShift6D: Multimodal RGB-D Benchmarking for Robust 6D Pose Estimation across Environment and Sensor Variations
A Comprehensive Benchmark of Computational Models for Antibody--Antigen Binding Affinity
Denoising Mutual Knowledge Distillation in Bi-Directional Multiple Instance Learning
GAnnET: A principled approach to automatically annotating charge stability diagrams
Visual-Language Model Knowledge Distillation Method for Image Quality Assessment
Visual Place Recognition for Large-Scale UAV Applications
MUPAX: Multidimensional Problem Agnostic eXplainable AI
Uncertainty-Aware Cross-Modal Knowledge Distillation with Prototype Learning for Multimodal Brain-Computer Interfaces
Disentangling coincident cell events using deep transfer learning and compressive sensing
Training Transformers with Enforced Lipschitz Constants
DVFL-Net: A Lightweight Distilled Video Focal Modulation Network for Spatio-Temporal Action Recognition
HanjaBridge: Resolving Semantic Ambiguity in Korean LLMs via Hanja-Augmented Pre-Training
A Multi-View High-Resolution Foot-Ankle Complex Point Cloud Dataset During Gait for Occlusion-Robust 3D Completion
DCR: Quantifying Data Contamination in LLMs Evaluation
FLsim: A Modular and Library-Agnostic Simulation Framework for Federated Learning
DrafterBench: Benchmarking Large Language Models for Tasks Automation in Civil Engineering
Feature Distillation is the Better Choice for Model-Heterogeneous Federated Learning
MLAR: Multi-layer Large Language Model-based Robotic Process Automation Applicant Tracking
Benchmarking and Evaluation of AI Models in Biology: Outcomes and Recommendations from the CZI Virtual Cells Workshop
CodeJudgeBench: Benchmarking LLM-as-a-Judge for Coding Tasks
CodeAssistBench (CAB): Dataset & Benchmarking for Multi-turn Chat-Based Code Assistance
Ref-Long: Benchmarking the Long-context Referencing Capability of Long-context Language Models
Calibrated and Robust Foundation Models for Vision-Language and Medical Image Tasks Under Distribution Shift
KAT-V1: Kwai-AutoThink Technical Report
SFedKD: Sequential Federated Learning with Discrepancy-Aware Multi-Teacher Knowledge Distillation
Towards Collaborative Fairness in Federated Learning Under Imbalanced Covariate Shift
Identifying the Smallest Adversarial Load Perturbations that Render DC-OPF Infeasible
Benchmarking Waitlist Mortality Prediction in Heart Transplantation Through Time-to-Event Modeling using New Longitudinal UNOS Dataset
Orchestrator-Agent Trust: A Modular Agentic AI Visual Classification System with Trust-Aware Orchestration and RAG-Based Reasoning
Hyperspectral Anomaly Detection Methods: A Survey and Comparative Study
SQLBarber: A System Leveraging Large Language Models to Generate Customized and Realistic SQL Workloads
A Systematic Analysis of Hybrid Linear Attention
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.