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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/qianjiangcn/eh-dnas

공식 코드

EH-DNAS: End-to-End Hardware-aware Differentiable Neural Architecture Search

https://github.com/qianlong0502/DKDM

공식 코드

DKDM: Data-Free Knowledge Distillation for Diffusion Models with Any Architecture

https://github.com/qijiang-qj/scimf

공식 코드

Learning collective multi-cellular dynamics from temporal scRNA-seq via a transformer-enhanced Neural SDE

https://github.com/qing-yuan233/RMCBench

공식 코드

RMCBench: Benchmarking Large Language Models' Resistance to Malicious Code

https://github.com/qiufan319/benchmark_pc_attack

공식 코드

Benchmarking and Analyzing Robust Point Cloud Recognition: Bag of Tricks for Defending Adversarial Examples

https://github.com/qiuhuachuan/CensorChat

공식 코드

Facilitating NSFW Text Detection in Open-Domain Dialogue Systems via Knowledge Distillation

https://github.com/qiuhuachuan/interactive-agents

공식 코드

Interactive Agents: Simulating Counselor-Client Psychological Counseling via Role-Playing LLM-to-LLM Interactions

https://github.com/qiuyu96/carver

공식 코드

Benchmarking and Analyzing 3D-aware Image Synthesis with a Modularized Codebase

https://github.com/qsong-github/scdrugmap

공식 코드

scDrugMap: Benchmarking Large Foundation Models for Drug Response Prediction

https://github.com/quark-framework/quark

공식 코드

Application-Oriented Benchmarking of Quantum Generative Learning Using QUARK

https://github.com/qub-blesson/Scission

공식 코드

Scission: Performance-driven and Context-aware Cloud-Edge Distribution of Deep Neural Networks

https://github.com/quel-imaging/quel-qal

공식 코드

Fluorescence Reference Target Quantitative Analysis Library

https://github.com/quiil/benchmarkingpathologyfoundationmodels

공식 코드

Benchmarking Pathology Foundation Models: Adaptation Strategies and Scenarios

https://github.com/quiil/quiil_thompson_solution

공식 코드

QuIIL at T3 challenge: Towards Automation in Life-Saving Intervention Procedures from First-Person View

https://github.com/qunhualilab/llm-ds-reproducibility

공식 코드

An Analyst-Inspector Framework for Evaluating Reproducibility of LLMs in Data Science

https://github.com/quqxui/medlasa

공식 코드

Editing Factual Knowledge and Explanatory Ability of Medical Large Language Models

https://github.com/qutech-delft/qgym

공식 코드

qgym: A Gym for Training and Benchmarking RL-Based Quantum Compilation

https://github.com/qznan/spankl

공식 코드

A Neural Span-Based Continual Named Entity Recognition Model

https://github.com/rabbit-magic-wh/InDL

공식 코드

InDL: A New Dataset and Benchmark for In-Diagram Logic Interpretation based on Visual Illusion

https://github.com/racai-ai/romanian-distilbert

공식 코드

Distilling the Knowledge of Romanian BERTs Using Multiple Teachers

https://github.com/radzim/obfuscated

공식 코드

LLM Performance for Code Generation on Noisy Tasks

https://github.com/ragib-amin-nihal/c2a

공식 코드

UAV-Enhanced Combination to Application: Comprehensive Analysis and Benchmarking of a Human Detection Dataset for Disaster Scenarios

https://github.com/ragmeh11/qu-brats

공식 코드

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

https://github.com/rahulsrma26/phonetic-word-embedding

공식 코드

Phonetic Word Embeddings

https://github.com/raiiyf/diffusets_exp

공식 코드

DiffuSETS: 12-lead ECG Generation Conditioned on Clinical Text Reports and Patient-Specific Information

https://github.com/rainavyas/attack-comparative-assessment

공식 코드

Is LLM-as-a-Judge Robust? Investigating Universal Adversarial Attacks on Zero-shot LLM Assessment

https://github.com/rainysponge/split-learning-attacks

공식 코드

SLPerf: a Unified Framework for Benchmarking Split Learning

https://github.com/raison-lab/g2d

공식 코드

G$^{2}$D: Boosting Multimodal Learning with Gradient-Guided Distillation

https://github.com/ralschoop/deepus

공식 코드

Experimental Validation of Ultrasound Beamforming with End-to-End Deep Learning for Single Plane Wave Imaging

https://github.com/ralucaj/dermx-benchmark

공식 코드

Dermatological Diagnosis Explainability Benchmark for Convolutional Neural Networks

https://github.com/ram95d/ovose

공식 코드

OVOSE: Open-Vocabulary Semantic Segmentation in Event-Based Cameras

https://github.com/rambodazimi/kd-lora

공식 코드

KD-LoRA: A Hybrid Approach to Efficient Fine-Tuning with LoRA and Knowledge Distillation

https://github.com/ramdrop/edgevl

공식 코드

Self-Adapting Large Visual-Language Models to Edge Devices across Visual Modalities

https://github.com/rampasek/HGNet

공식 코드

Hierarchical graph neural nets can capture long-range interactions

https://github.com/random2314235/atsc

공식 코드

Adaptive Teaching with Shared Classifier for Knowledge Distillation

https://github.com/randyzhouran/conner

공식 코드

ConNER: Consistency Training for Cross-lingual Named Entity Recognition

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