https://github.com/oppo-mente-lab/facescore
공식 코드FaceScore: Benchmarking and Enhancing Face Quality in Human Generation
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/optimai-lab/memory_efficient_pretraining
공식 코드https://github.com/optml-group/diffusion-mu-attack
공식 코드https://github.com/orangeinsouth/pareto-mutual-distillation
공식 코드Towards Higher Pareto Frontier in Multilingual Machine Translation
https://github.com/orashi/AlacGAN
공식 코드User-Guided Deep Anime Line Art Colorization with Conditional Adversarial Networks
https://github.com/orashi/NSE_NAS
공식 코드https://github.com/orion-ai-lab/efficientbigearthnet
공식 코드Benchmarking and scaling of deep learning models for land cover image classification
https://github.com/orion-ai-lab/wildfirespread
공식 코드https://github.com/orpgol/decentralized_models
공식 코드https://github.com/oscarxzq/weight-selection
공식 코드https://github.com/oshizo/japaneseembeddingeval
공식 코드https://github.com/oskoller/sign-language-state-of-the-art
공식 코드Quantitative Survey of the State of the Art in Sign Language Recognition
https://github.com/ososos888/prune-then-distill
공식 코드https://github.com/oss-bench/oss-bench
공식 코드https://github.com/ostars/ruie
공식 코드RUIE: Retrieval-based Unified Information Extraction using Large Language Model
https://github.com/osu-nlp-group/redteamcua
공식 코드RedTeamCUA: Realistic Adversarial Testing of Computer-Use Agents in Hybrid Web-OS Environments
https://github.com/osvai/af-dcd
공식 코드Augmentation-Free Dense Contrastive Knowledge Distillation for Efficient Semantic Segmentation
https://github.com/osvai/norm
공식 코드NORM: Knowledge Distillation via N-to-One Representation Matching
https://github.com/osvai/ssd-kd
공식 코드https://github.com/otroshi/synthdistill
공식 코드SynthDistill: Face Recognition with Knowledge Distillation from Synthetic Data
https://github.com/ouc-ocean-group/CTKD
공식 코드Highlight Every Step: Knowledge Distillation via Collaborative Teaching
https://github.com/oucvisiongroup/watermono
공식 코드https://github.com/owkin/hte
공식 코드Subgroup analysis methods for time-to-event outcomes in heterogeneous randomized controlled trials
https://github.com/oxai/visogender
공식 코드VisoGender: A dataset for benchmarking gender bias in image-text pronoun resolution
https://github.com/oxwhirl/pymarl
공식 코드https://github.com/oxwhirl/smac
공식 코드https://github.com/padas-lab-de/multi-language-dataset-creator
공식 코드https://github.com/paddlepaddle/rocketqa
공식 코드PAIR: Leveraging Passage-Centric Similarity Relation for Improving Dense Passage Retrieval
https://github.com/pair-code/interpretability
공식 코드Visualizing Linguistic Diversity of Text Datasets Synthesized by Large Language Models
https://github.com/palaashagrawal/llmgraphtraversal
공식 코드https://github.com/palash1992/GEM-Benchmark
공식 코드https://github.com/palin2018/browsecomp-zh
공식 코드BrowseComp-ZH: Benchmarking Web Browsing Ability of Large Language Models in Chinese
https://github.com/panagiotamoraiti/continual-learning-challenge-2024
공식 코드https://github.com/panliangrui/nips2025
공식 코드https://github.com/panmt/vid2act
공식 코드Model-Based Reinforcement Learning with Multi-Task Offline Pretraining
https://github.com/paopaolin/papercode/tree/main/MENGGUANLIN_papercode/combine%20V1
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.