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Data-free Knowledge Distillation
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/illaitar/based
공식 코드BASED: Benchmarking, Analysis, and Structural Estimation of Deblurring
https://github.com/illidanlab/abd
공식 코드Revisiting Data-Free Knowledge Distillation with Poisoned Teachers
https://github.com/ilyatyagin/dyport
공식 코드Dyport: Dynamic Importance-based Hypothesis Generation Benchmarking Technique
https://github.com/imatif17/acia
공식 코드Attention-based Class-Conditioned Alignment for Multi-Source Domain Adaptation of Object Detectors
https://github.com/imeanai/webcanvas
공식 코드https://github.com/imics-lab/positional-encoding-benchmark
공식 코드Positional Encoding in Transformer-Based Time Series Models: A Survey
https://github.com/iml-dkfz/fd-shifts
공식 코드A Call to Reflect on Evaluation Practices for Failure Detection in Image Classification
https://github.com/imnearth/autojudger
공식 코드AutoJudger: An Agent-Driven Framework for Efficient Benchmarking of MLLMs
https://github.com/impresso/paraphrasus
공식 코드PARAPHRASUS : A Comprehensive Benchmark for Evaluating Paraphrase Detection Models
https://github.com/indobenchmark/indonlu
공식 코드IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding
https://github.com/indyfree/CARLA
공식 코드CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
https://github.com/infiagent/infiagent
공식 코드https://github.com/infinigence/lveval
공식 코드LV-Eval: A Balanced Long-Context Benchmark with 5 Length Levels Up to 256K
https://github.com/inigoval/fixmatch
공식 코드https://github.com/ink7-sudo/dependeval
공식 코드DependEval: Benchmarking LLMs for Repository Dependency Understanding
https://github.com/inm-6/bennch
공식 코드A Modular Workflow for Performance Benchmarking of Neuronal Network Simulations
https://github.com/innodatalabs/innodata-llm-safety
공식 코드https://github.com/inouye-lab/feddg_benchmark
공식 코드https://github.com/inspirai/wilderness-scavenger
공식 코드WILD-SCAV: Benchmarking FPS Gaming AI on Unity3D-based Environments
https://github.com/instadeepai/compass
공식 코드Combinatorial Optimization with Policy Adaptation using Latent Space Search
https://github.com/instadeepai/debatellm
공식 코드Should we be going MAD? A Look at Multi-Agent Debate Strategies for LLMs
https://github.com/intelligent-computing-lab-yale/quanitzation-aware-snn-training-and-hardware-evaluation-for-imc-architectures
공식 코드https://github.com/interaction-lab/hribench
공식 코드https://github.com/interactivenlp-team/rolellm-public
공식 코드RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models
https://github.com/intmegroup/lmm4lmm
공식 코드LMM4LMM: Benchmarking and Evaluating Large-multimodal Image Generation with LMMs
https://github.com/intmegroup/love
공식 코드LOVE: Benchmarking and Evaluating Text-to-Video Generation and Video-to-Text Interpretation
https://github.com/iocuydi/amharic-llama-llava
공식 코드Amharic LLaMA and LLaVA: Multimodal LLMs for Low Resource Languages
https://github.com/ioverho/misinfo-general
공식 코드https://github.com/ipolharvard/ethos-ares
공식 코드Foundation Model of Electronic Medical Records for Adaptive Risk Estimation
https://github.com/ipouyall/benchmarking_chatgpt_for_persian
공식 코드Benchmarking Large Language Models for Persian: A Preliminary Study Focusing on ChatGPT
https://github.com/irmlma/mobility-simulation-cdpm
공식 코드Synthetic location trajectory generation using categorical diffusion models
https://github.com/is2lab/s-eval
공식 코드S-Eval: Towards Automated and Comprehensive Safety Evaluation for Large Language Models
https://github.com/isayevlab/geom-drugs-3dgen-evaluation
공식 코드GEOM-Drugs Revisited: Toward More Chemically Accurate Benchmarks for 3D Molecule Generation
https://github.com/ishikura-a/aug-kd
공식 코드AuG-KD: Anchor-Based Mixup Generation for Out-of-Domain Knowledge Distillation
https://github.com/isle-dev/ecbd
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.