https://github.com/huster-wgm/geoseg
공식 코드Geoseg: A Computer Vision Package for Automatic Building Segmentation and Outline Extraction
Papers with Code 아카이브
Computational Efficiency
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Methods and optimizations to reduce the computational resources (e.g., time, memory, or power) needed for training and inference in models. This involves techniques that streamline processing, optimize algorithms, or leverage hardware to enhance performance without compromising accuracy.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/hwq0726/sefd
공식 코드SEFD: Semantic-Enhanced Framework for Detecting LLM-Generated Text
https://github.com/hwwang55/GraphGAN
공식 코드GraphGAN: Graph Representation Learning with Generative Adversarial Nets
https://github.com/hyeon-jo/interchange-transfer-kd
공식 코드itKD: Interchange Transfer-based Knowledge Distillation for 3D Object Detection
https://github.com/hysonlab/multiscalehsi
공식 코드MOB-GCN: A Novel Multiscale Object-Based Graph Neural Network for Hyperspectral Image Classification
https://github.com/hyu-nlp/hyper-cl
공식 코드Hyper-CL: Conditioning Sentence Representations with Hypernetworks
https://github.com/hyunbo9/domain-reduction-strategy
공식 코드https://github.com/hyunwoo137/edaformer
공식 코드Embedding-Free Transformer with Inference Spatial Reduction for Efficient Semantic Segmentation
https://github.com/hyunwoo137/metaseg
공식 코드MetaSeg: MetaFormer-based Global Contexts-aware Network for Efficient Semantic Segmentation
https://github.com/iago-suarez/beblid
공식 코드https://github.com/icon-lab/FD-Net
공식 코드FD-Net: An Unsupervised Deep Forward-Distortion Model for Susceptibility Artifact Correction in EPI
https://github.com/idea-iitd/gnnmerge
공식 코드GNNMerge: Merging of GNN Models Without Accessing Training Data
https://github.com/idiap/mhan
공식 코드Multilingual Hierarchical Attention Networks for Document Classification
https://github.com/iesl/dilated-cnn-ner
공식 코드Fast and Accurate Entity Recognition with Iterated Dilated Convolutions
https://github.com/iis-esslingen/nerf-3dgs-benchmark
공식 코드https://github.com/ilareina/fullyattentional
공식 코드https://github.com/illuin-tech/contextual-embeddings
공식 코드Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings
https://github.com/imageomics/finer-cam
공식 코드Finer-CAM: Spotting the Difference Reveals Finer Details for Visual Explanation
https://github.com/imoonlab/yolov13
공식 코드YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception
https://github.com/indussky8/demo_ProLFA
공식 코드ProLFA: Representative Prototype Selection for Local Feature Aggregation
https://github.com/inouye-lab/saub
공식 코드Expressive Score-Based Priors for Distribution Matching with Geometry-Preserving Regularization
https://github.com/insuhan/calibquant
공식 코드https://github.com/intel/intel-extension-for-transformers
공식 코드https://github.com/intelligentEarth/surrogateBayeslands
공식 코드Surrogate-assisted Bayesian inversion for landscape and basin evolution models
https://github.com/intellistream/efficient-stream-learning
공식 코드StreamFP: Learnable Fingerprint-guided Data Selection for Efficient Stream Learning
https://github.com/internlandmark/flashgs
공식 코드FlashGS: Efficient 3D Gaussian Splatting for Large-scale and High-resolution Rendering
https://github.com/internlandmark/landmarksystem
공식 코드https://github.com/interpretty/ammunet
공식 코드AMMUNet: Multi-Scale Attention Map Merging for Remote Sensing Image Segmentation
https://github.com/ipo-lab/scqpth
공식 코드SCQPTH: an efficient differentiable splitting method for convex quadratic programming
https://github.com/ipo-lab/scqpth_bench
공식 코드SCQPTH: an efficient differentiable splitting method for convex quadratic programming
https://github.com/ipsitmantri/digraf
공식 코드https://github.com/irina-b1/KAN_TS
공식 코드Exploring Kolmogorov-Arnold Networks for Interpretable Time Series Classification
https://github.com/ischenawei/dmnil
공식 코드Without Paired Labeled Data: An End-to-End Self-Supervised Paradigm for UAV-View Geo-Localization
https://github.com/ist-daslab/quik
공식 코드QUIK: Towards End-to-End 4-Bit Inference on Generative Large Language Models
https://github.com/itsnotacie/information-fusion-sglp
공식 코드SGLP: A Similarity Guided Fast Layer Partition Pruning for Compressing Large Deep Models
https://github.com/itsnotacie/sepprune
공식 코드SepPrune: Structured Pruning for Efficient Deep Speech Separation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.