https://github.com/xingshuaihuang/dtlight
공식 코드Traffic Signal Control Using Lightweight Transformers: An Offline-to-Online RL Approach
Papers with Code 아카이브
Knowledge Distillation
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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/xinjin95/binsum
공식 코드Binary Code Summarization: Benchmarking ChatGPT/GPT-4 and Other Large Language Models
https://github.com/xinleihe/mgtbench
공식 코드https://github.com/xinxingwu-uk/dgae
공식 코드Deepened Graph Auto-Encoders Help Stabilize and Enhance Link Prediction
https://github.com/xiutian/orchid
공식 코드https://github.com/xl2248/d2tv
공식 코드https://github.com/xlang-ai/OSWorld
공식 코드OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments
https://github.com/xlhex/gal_syntex
공식 코드https://github.com/xliuhw/NLU-Evaluation-Data
공식 코드Benchmarking Natural Language Understanding Services for building Conversational Agents
https://github.com/xmed-lab/fopro-kd
공식 코드https://github.com/xmindflow/braincl
공식 코드Modality-Independent Brain Lesion Segmentation with Privacy-aware Continual Learning
https://github.com/xnhyacinth/nesycd
공식 코드https://github.com/xq141839/esp-medsam
공식 코드ESP-MedSAM: Efficient Self-Prompting SAM for Universal Domain-Generalized Medical Image Segmentation
https://github.com/xsysigma/tencentllmeval
공식 코드TencentLLMEval: A Hierarchical Evaluation of Real-World Capabilities for Human-Aligned LLMs
https://github.com/xt-cyh/codi-eval
공식 코드Benchmarking Large Language Models on Controllable Generation under Diversified Instructions
https://github.com/xtotodilex/deep-learning-model-optimization
공식 코드https://github.com/xuanjixiao/onerec
공식 코드Social4Rec: Distilling User Preference from Social Graph for Video Recommendation in Tencent
https://github.com/xuanlinli17/large_vlm_distillation_ood
공식 코드Distilling Large Vision-Language Model with Out-of-Distribution Generalizability
https://github.com/xucong-parsifal/lightskd
공식 코드Lightweight Self-Knowledge Distillation with Multi-source Information Fusion
https://github.com/xuejunsun98/hr-vilage-3k3m
공식 코드https://github.com/xuez-phd/efficient-rgb-t-early-fusion-detection
공식 코드Rethinking Early-Fusion Strategies for Improved Multispectral Object Detection
https://github.com/xuez-phd/focusnet-classifying-better-by-focusing-on-confusing-classes
공식 코드FocusNet: Classifying Better by Focusing on Confusing Classes
https://github.com/xuguodong03/SSKD
공식 코드https://github.com/xuguodong03/UNIXKD
공식 코드Computation-Efficient Knowledge Distillation via Uncertainty-Aware Mixup
https://github.com/xuguodong03/stylekd
공식 코드https://github.com/xupei0610/KDMA
공식 코드Human-Inspired Multi-Agent Navigation using Knowledge Distillation
https://github.com/xweiq/benchmark-graphfairness
공식 코드Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New Benchmark
https://github.com/xxgege/nico-plus
공식 코드NICO++: Towards Better Benchmarking for Domain Generalization
https://github.com/xxxiaol/qrdata
공식 코드https://github.com/xxyQwQ/ComfyBench
공식 코드https://github.com/xy-showing/amqa
공식 코드AMQA: An Adversarial Dataset for Benchmarking Bias of LLMs in Medicine and Healthcare
https://github.com/xybfight/gnarkd
공식 코드https://github.com/xybfight/vrp-generalization
공식 코드https://github.com/xyupeng/beta
공식 코드Divide to Adapt: Mitigating Confirmation Bias for Domain Adaptation of Black-Box Predictors
https://github.com/xyzcs/scireplicate-bench
공식 코드SciReplicate-Bench: Benchmarking LLMs in Agent-driven Algorithmic Reproduction from Research Papers
https://github.com/y-babdalla/vect_gan
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.