SOTAVerified

Computational Efficiency

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.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 631–640 of 4891 papers

TitleStatusHype
DeepSeeColor: Realtime Adaptive Color Correction for Autonomous Underwater Vehicles via Deep Learning MethodsCode1
A Simple Local Minimal Intensity Prior and An Improved Algorithm for Blind Image DeblurringCode1
FTMixer: Frequency and Time Domain Representations Fusion for Time Series ModelingCode1
Fully 11 Convolutional Network for Lightweight Image Super-ResolutionCode1
FViT: A Focal Vision Transformer with Gabor FilterCode1
GaraMoSt: Parallel Multi-Granularity Motion and Structural Modeling for Efficient Multi-Frame Interpolation in DSA ImagesCode1
Decoupling Spatio-Temporal Prediction: When Lightweight Large Models Meet Adaptive HypergraphsCode1
Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR)Code1
DecoupleNet: A Lightweight Backbone Network With Efficient Feature Decoupling for Remote Sensing Visual TasksCode1
Deep Implicit Moving Least-Squares Functions for 3D ReconstructionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified