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Data-free Knowledge Distillation

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하위 태스크: 1
평가표: 2
보존된 평가 기록: 8

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/bmucsanyi/bud

공식 코드

Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for Specialized Tasks

https://github.com/bmucsanyi/untangle

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Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for Specialized Tasks

https://github.com/bnn-upc/graphlaxy

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Bias Reduction via Cooperative Bargaining in Synthetic Graph Dataset Generation

https://github.com/bobxwu/antileak-bench

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AntiLeak-Bench: Preventing Data Contamination by Automatically Constructing Benchmarks with Updated Real-World Knowledge

https://github.com/boevalab/scssl-bench

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scSSL-Bench: Benchmarking Self-Supervised Learning for Single-Cell Data

https://github.com/boleima/topro

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ToPro: Token-Level Prompt Decomposition for Cross-Lingual Sequence Labeling Tasks

https://github.com/booml247/ctlsan

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C-TLSAN: Content-Enhanced Time-Aware Long- and Short-Term Attention Network for Personalized Recommendation

https://github.com/boschresearch/causalassembly

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$\texttt{causalAssembly}$: Generating Realistic Production Data for Benchmarking Causal Discovery

https://github.com/boschresearch/mlfuzz

공식 코드

Revisiting Neural Program Smoothing for Fuzzing

https://github.com/boschresearch/neuzzplusplus

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Revisiting Neural Program Smoothing for Fuzzing

https://github.com/bowang-lab/bleep

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Spatially Resolved Gene Expression Prediction from H&E Histology Images via Bi-modal Contrastive Learning

https://github.com/bowang-lab/medsam

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Segment Anything in Medical Images and Videos: Benchmark and Deployment

https://github.com/bowen-upenn/personamem

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Know Me, Respond to Me: Benchmarking LLMs for Dynamic User Profiling and Personalized Responses at Scale

https://github.com/bowenl0218/mmvc_video_codec

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MMVC: Learned Multi-Mode Video Compression with Block-based Prediction Mode Selection and Density-Adaptive Entropy Coding

https://github.com/braindatalab/gecobench

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GECOBench: A Gender-Controlled Text Dataset and Benchmark for Quantifying Biases in Explanations

https://github.com/brandonhanx/textreid

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Text-Based Person Search with Limited Data

https://github.com/braqiiit/qrealboost

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Quantum Boosting using Domain-Partitioning Hypotheses

https://github.com/brave-experiments/melt-public

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MELTing point: Mobile Evaluation of Language Transformers

https://github.com/brentp/bwa-meth

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Fast and accurate alignment of long bisulfite-seq reads

https://github.com/brianxiang123/eegetcovariatedistributionalshift

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Vector-Based Data Improves Left-Right Eye-Tracking Classifier Performance After a Covariate Distributional Shift

https://github.com/brucelyu17/sc-tc-bench

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Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese

https://github.com/bryanlimy/ViV1T

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Retrospective for the Dynamic Sensorium Competition for predicting large-scale mouse primary visual cortex activity from videos

https://github.com/buds-lab/aldiplusplus

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ALDI++: Automatic and parameter-less discord and outlier detection for building energy load profiles

https://github.com/buds-lab/energystar-plus-plus

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EnergyStar++: Towards more accurate and explanatory building energy benchmarking

https://github.com/bupt-reasoning-lab/financereasoning

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FinanceReasoning: Benchmarking Financial Numerical Reasoning More Credible, Comprehensive and Challenging

https://github.com/bytedance-seed/bm-code

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ByteMorph: Benchmarking Instruction-Guided Image Editing with Non-Rigid Motions

https://github.com/bytedance/MTVQA

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MTVQA: Benchmarking Multilingual Text-Centric Visual Question Answering

https://github.com/bytedance/pargo

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MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

https://github.com/c2dc/t800

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Design and implementation of intelligent packet filtering in IoT microcontroller-based devices

https://github.com/caiyun-ai/gar

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Benchmarking and Understanding Compositional Relational Reasoning of LLMs

https://github.com/caizhongang/SMPLer-X

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SMPLer-X: Scaling Up Expressive Human Pose and Shape Estimation

https://github.com/calvin-zcx/scehr

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SCEHR: Supervised Contrastive Learning for Clinical Risk Prediction using Electronic Health Records

https://github.com/cambridgeltl/sapbert

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Self-Alignment Pretraining for Biomedical Entity Representations

https://github.com/camma-public/attention-tripnet

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Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet Datasets

https://github.com/camma-public/ivtmetrics

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Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet Datasets

https://github.com/camma-public/tripnet

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Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet Datasets

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