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 3591–3600 of 4891 papers

TitleStatusHype
Hybrid SNN-ANN: Energy-Efficient Classification and Object Detection for Event-Based Vision—0
Is Class-Incremental Enough for Continual Learning?—0
U2-Former: A Nested U-shaped Transformer for Image Restoration—0
Multi-scale Graph Convolutional Networks with Self-Attention—0
MT-TransUNet: Mediating Multi-Task Tokens in Transformers for Skin Lesion Segmentation and ClassificationCode1
Temporally Resolution Decrement: Utilizing the Shape Consistency for Higher Computational Efficiency—0
CondenseNeXt: An Ultra-Efficient Deep Neural Network for Embedded SystemsCode0
Shallow Network Based on Depthwise Over-Parameterized Convolution for Hyperspectral Image Classification—0
SimiGrad: Fine-Grained Adaptive Batching for Large Scale Training using Gradient Similarity MeasurementCode0
Hyperbolic Procrustes Analysis Using Riemannian GeometryCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified