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

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Showing 731–740 of 4891 papers

TitleStatusHype
Adaptive Focus for Efficient Video RecognitionCode1
Multi-Objective Evolutionary Design of Deep Convolutional Neural Networks for Image ClassificationCode1
BetterNet: An Efficient CNN Architecture with Residual Learning and Attention for Precision Polyp SegmentationCode1
Correlation Filters for Unmanned Aerial Vehicle-Based Aerial Tracking: A Review and Experimental EvaluationCode1
Corner Proposal Network for Anchor-free, Two-stage Object DetectionCode1
CosmoPower-JAX: high-dimensional Bayesian inference with differentiable cosmological emulatorsCode1
MUXConv: Information Multiplexing in Convolutional Neural NetworksCode1
Contrast-Phys+: Unsupervised and Weakly-supervised Video-based Remote Physiological Measurement via Spatiotemporal ContrastCode1
Adaptive Fourier Neural Operators: Efficient Token Mixers for TransformersCode1
Content-aware Token Sharing for Efficient Semantic Segmentation with Vision TransformersCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified