https://github.com/gorilla-lab-scut/qs3
공식 코드Quasi-Balanced Self-Training on Noise-Aware Synthesis of Object Point Clouds for Closing Domain Gap
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/gorilla-lab-scut/ttac
공식 코드Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering
https://github.com/gorjanradevski/multimodal-distillation
공식 코드https://github.com/gpbench/tinyversegp
공식 코드TinyverseGP: Towards a Modular Cross-domain Benchmarking Framework for Genetic Programming
https://github.com/gpt-fathom/gpt-fathom
공식 코드https://github.com/gpt4vision/OvSGTR
공식 코드https://github.com/graison-thomas/TinyFinBERT
공식 코드Enhancing TinyBERT for Financial Sentiment Analysis Using GPT-Augmented FinBERT Distillation
https://github.com/graph-axis/gcleaner
공식 코드"No Matter What You Do": Purifying GNN Models via Backdoor Unlearning
https://github.com/graph-com/gdl_ds
공식 코드GeSS: Benchmarking Geometric Deep Learning under Scientific Applications with Distribution Shifts
https://github.com/graph-com/gess
공식 코드GeSS: Benchmarking Geometric Deep Learning under Scientific Applications with Distribution Shifts
https://github.com/graph-com/layerdag
공식 코드LayerDAG: A Layerwise Autoregressive Diffusion Model for Directed Acyclic Graph Generation
https://github.com/graphdeeplearning/benchmarking-gnns
공식 코드https://github.com/graytowne/rank_distill
공식 코드Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System
https://github.com/gregoirepetit/echodfkd
공식 코드EchoDFKD: Data-Free Knowledge Distillation for Cardiac Ultrasound Segmentation using Synthetic Data
https://github.com/gregor-ge/foci-benchmark
공식 코드https://github.com/grispeut/udfa
공식 코드Robust and Accurate Object Detection via Self-Knowledge Distillation
https://github.com/gsarridis/indistill
공식 코드InDistill: Information flow-preserving knowledge distillation for model compression
https://github.com/gsgoncalves/emnlp2023_llm_compression_and_social_bias
공식 코드Understanding the Effect of Model Compression on Social Bias in Large Language Models
https://github.com/gsig/actions-for-actions
공식 코드What Actions are Needed for Understanding Human Actions in Videos?
https://github.com/gsiros/drop
공식 코드DROP: Poison Dilution via Knowledge Distillation for Federated Learning
https://github.com/gsisaoki/omnidirectional_blender_3d_dataset
공식 코드OB3D: A New Dataset for Benchmarking Omnidirectional 3D Reconstruction Using Blender
https://github.com/gsk-ai/leak-proof-cmap
공식 코드https://github.com/gt-salt/mic
공식 코드The Moral Integrity Corpus: A Benchmark for Ethical Dialogue Systems
https://github.com/gt-star-lab/jaxrobotarium
공식 코드JaxRobotarium: Training and Deploying Multi-Robot Policies in 10 Minutes
https://github.com/gtDMMB/pmfe2023
공식 코드Can geometric combinatorics improve RNA branching predictions?
https://github.com/gtfintechlab/worldcentralbanks
공식 코드Words That Unite The World: A Unified Framework for Deciphering Central Bank Communications Globally
https://github.com/gtfintechlab/zero-shot-finance
공식 코드Zero is Not Hero Yet: Benchmarking Zero-Shot Performance of LLMs for Financial Tasks
https://github.com/gtzheng/fewstab
공식 코드https://github.com/guanghaoyin/cvrkd-iqa
공식 코드Content-Variant Reference Image Quality Assessment via Knowledge Distillation
https://github.com/guanghaozhu663/skd-tstsan
공식 코드https://github.com/gugarosa/opf_distance
공식 코드Comparative Study Between Distance Measures On Supervised Optimum-Path Forest Classification
https://github.com/guiliang/cirl-benchmarks-public
공식 코드Benchmarking Constraint Inference in Inverse Reinforcement Learning
https://github.com/guiliang/icrl-benchmarks-public
공식 코드Benchmarking Constraint Inference in Inverse Reinforcement Learning
https://github.com/gulnazaki/counterfactual-benchmark
공식 코드https://github.com/guobaoxiao/dsam
공식 코드Exploring Deeper! Segment Anything Model with Depth Perception for Camouflaged Object Detection
https://github.com/guojiaqi-1020/medivlad
공식 코드Efficient Lung Ultrasound Severity Scoring Using Dedicated Feature Extractor
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.