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Efficient Neural Network
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/kjetil-lye/learning_airfoils
공식 코드https://github.com/kzajac97/frequency-supported-neural-networks
공식 코드Orthogonal Transforms in Neural Networks Amount to Effective Regularization
https://github.com/liuyangmage/in-context-operator-networks
공식 코드Fine-Tune Language Models as Multi-Modal Differential Equation Solvers
https://github.com/luke-mcdermott-mi/distilled-pruning
공식 코드https://github.com/megvii-research/PMRID
공식 코드https://github.com/microsoft/geta
공식 코드https://github.com/mlvlab/EfficientViM
공식 코드EfficientViM: Efficient Vision Mamba with Hidden State Mixer based State Space Duality
https://github.com/muradtuk/provable-data-subset-selection-for-efficient-neural-network-training
공식 코드Provable Data Subset Selection For Efficient Neural Network Training
https://github.com/nblt/twa
공식 코드Trainable Weight Averaging: A General Approach for Subspace Training
https://github.com/nhjydywd/octa-frnet
공식 코드An Accurate and Efficient Neural Network for OCTA Vessel Segmentation and a New Dataset
https://github.com/nnstreamer/nntrainer
공식 코드A New Frontier of AI: On-Device AI Training and Personalization
https://github.com/orgoro/white-2-black
공식 코드White-to-Black: Efficient Distillation of Black-Box Adversarial Attacks
https://github.com/pranavphoenix/WaveMix
공식 코드WaveMix: A Resource-efficient Neural Network for Image Analysis
https://github.com/pranavphoenix/WaveMixSR
공식 코드WaveMixSR: A Resource-efficient Neural Network for Image Super-resolution
https://github.com/pulp-platform/fann-on-mcu
공식 코드https://github.com/rwightman/pytorch-image-models
공식 코드https://github.com/saikrishna-prathapaneni/LowDINO
공식 코드https://github.com/shubha07m/Energy-optimized-semantic-loss
공식 코드Transformers for Green Semantic Communication: Less Energy, More Semantics
https://github.com/shyamven/learn2mix
공식 코드Learn2Mix: Training Neural Networks Using Adaptive Data Integration
https://github.com/steven-tel/cen-hdr
공식 코드CEN-HDR: Computationally Efficient neural Network for real-time High Dynamic Range imaging
https://github.com/ternencewu123/gautomri
공식 코드https://github.com/unic-lab/qedgix
공식 코드GNN-Empowered Effective Partial Observation MARL Method for AoI Management in Multi-UAV Network
https://github.com/vahidzee/ennelut
공식 코드Efficient Neural Network Encoding for 3D Color Lookup Tables
https://github.com/x-zho14/VRPGE-Sparse-Training
공식 코드Efficient Neural Network Training via Forward and Backward Propagation Sparsification
https://github.com/xianggebenben/dlADMM
공식 코드A Convergent ADMM Framework for Efficient Neural Network Training
https://github.com/yanghr/DeepHoyer
공식 코드DeepHoyer: Learning Sparser Neural Network with Differentiable Scale-Invariant Sparsity Measures
https://github.com/yangshunzhi1994/EdgeCNN
공식 코드EdgeCNN: Convolutional Neural Network Classification Model with small inputs for Edge Computing
https://github.com/yefanzhou/threeregimepruning
공식 코드https://github.com/yu-zhewen/mixed-td
공식 코드Mixed-TD: Efficient Neural Network Accelerator with Layer-Specific Tensor Decomposition
https://gitlab.com/jrsassen/freaky-torus
공식 코드https://gitlab.com/pannadevs/panna
공식 코드PANNA 2.0: Efficient neural network interatomic potentials and new architectures
https://gitlab.com/viper-purdue/efficient-compression
공식 코드On Efficient Neural Network Architectures for Image Compression
https://github.com/2023-MindSpore-1/ms-code-213/tree/main/E-NET
ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
https://github.com/2023-MindSpore-4/Code-5/tree/main/lenet
Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures
https://github.com/2023-MindSpore-4/Code3/tree/main/E-NET
ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.