https://github.com/wuyou3474/ortrack
공식 코드Learning Occlusion-Robust Vision Transformers for Real-Time UAV Tracking
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/wwh0411/fedmabench
공식 코드FedMABench: Benchmarking Mobile Agents on Decentralized Heterogeneous User Data
https://github.com/wwh0411/knowledgesg
공식 코드KnowledgeSG: Privacy-Preserving Synthetic Text Generation with Knowledge Distillation from Server
https://github.com/wwxb2012/digital_staining_knowledge_distillation
공식 코드https://github.com/wxl-lxw/stringllm
공식 코드StringLLM: Understanding the String Processing Capability of Large Language Models
https://github.com/wyc-ruiker/hkd
공식 코드https://github.com/wywywang/coachai-projects
공식 코드Benchmarking Stroke Forecasting with Stroke-Level Badminton Dataset
https://github.com/wyze-AI/AdaptiveDistillation
공식 코드Adaptive Distillation: Aggregating Knowledge from Multiple Paths for Efficient Distillation
https://github.com/x-lance/medical-dataset
공식 코드CSS: A Large-scale Cross-schema Chinese Text-to-SQL Medical Dataset
https://github.com/x66ccff/swarmbench
공식 코드https://github.com/xabo-rb/benchmarking_files
공식 코드https://github.com/xanaduai/qml-benchmarks
공식 코드Better than classical? The subtle art of benchmarking quantum machine learning models
https://github.com/xdshen-ai/benchmark-of-code-migration
공식 코드CODEMENV: Benchmarking Large Language Models on Code Migration
https://github.com/xeniaohmer/multisense_consistency
공식 코드https://github.com/xgraphing/xflow
공식 코드https://github.com/xhtai/clusterTruster
공식 코드https://github.com/xiagll/optiforest
공식 코드https://github.com/xianfengwu01/lightgen
공식 코드https://github.com/xiangly55/LFME
공식 코드Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-tailed Classification
https://github.com/xiangyanfei212/RMBench-2022
공식 코드RMBench: Benchmarking Deep Reinforcement Learning for Robotic Manipulator Control
https://github.com/xiangyuan-zhang/controlgym
공식 코드Controlgym: Large-Scale Control Environments for Benchmarking Reinforcement Learning Algorithms
https://github.com/xiao-jian-zi/mu-goldfish-an-efficient-federated-unlearning-framework
공식 코드https://github.com/xiaoachen98/DDB
공식 코드Deliberated Domain Bridging for Domain Adaptive Semantic Segmentation
https://github.com/xiaobin-xs/EEG-competing-risks
공식 코드https://github.com/xiaohao-xu/slam-under-perturbation
공식 코드Customizable Perturbation Synthesis for Robust SLAM Benchmarking
https://github.com/xiaojihh/cl_all-in-one
공식 코드Continual All-in-One Adverse Weather Removal with Knowledge Replay on a Unified Network Structure
https://github.com/xiaomabufei/skdf
공식 코드https://github.com/xiaoman-zhang/PMC-VQA
공식 코드PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering
https://github.com/xiaoni-61/dh-benchmark
공식 코드Benchmarking Data Heterogeneity Evaluation Approaches for Personalized Federated Learning
https://github.com/xiaowu0162/longmemeval
공식 코드LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory
https://github.com/xiaoyihan6/ms-fsdb
공식 코드https://github.com/xiaxin1998/eodrec
공식 코드https://github.com/xiaxin1998/od-rec
공식 코드On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation
https://github.com/xinchen9/cot_distillation_acl2024
공식 코드Learning to Maximize Mutual Information for Chain-of-Thought Distillation
https://github.com/xinghaochen/tinysam
공식 코드TinySAM: Pushing the Envelope for Efficient Segment Anything Model
https://github.com/xingqunqi-lab/gkd-framework
공식 코드Exploring Generalizable Distillation for Efficient Medical Image Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.