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Found 8 papers, 2 papers with code
USDC: Unified Static and Dynamic Compression for Visual Transformer
To alleviate two disadvantages of two categories of methods, we propose to unify the static compression and dynamic compression techniques jointly to obtain an input-adaptive compressed model, which can further better balance the total compression ratios and the model performances.
Resource Constrained Model Compression via Minimax Optimization for Spiking Neural Networks
We propose an improved end-to-end Minimax optimization method for this sparse learning problem to better balance the model performance and the computation efficiency.
GDP: Stabilized Neural Network Pruning via Gates with Differentiable Polarization
During the training process, the polarization effect will drive a subset of gates to smoothly decrease to exact zero, while other gates gradually stay away from zero by a large margin.
Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification with Chest CT
We evaluated our proposed AFS-DF on COVID-19 dataset with 1495 patients of COVID-19 and 1027 patients of community acquired pneumonia (CAP).
Dual-Sampling Attention Network for Diagnosis of COVID-19 from Community Acquired Pneumonia
To this end, we develop a dual-sampling attention network to automatically diagnose COVID- 19 from the community acquired pneumonia (CAP) in chest computed tomography (CT).
Diagnosis of Coronavirus Disease 2019 (COVID-19) with Structured Latent Multi-View Representation Learning
In this study, we propose to conduct the diagnosis of COVID-19 with a series of features extracted from CT images.
Large-Scale Screening of COVID-19 from Community Acquired Pneumonia using Infection Size-Aware Classification
The worldwide spread of coronavirus disease (COVID-19) has become a threatening risk for global public health.
