Official Pytorch implementation of the paper "Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification" (NeurIPS 2022)
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Updated
Nov 18, 2022 - Python
Official Pytorch implementation of the paper "Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification" (NeurIPS 2022)
SE-PseudoGrid for the AutoCon journal paper.
ADHDeepNet is a model that integrates temporal and spatial characterization, attention modules, and explainability techniques, optimized for EEG data ADAD diagnosis. Neural Architecture Search (NAS), Hyper-parameter optimization, and data augmentation are also incorporated to enhance the model's performance and accuracy.
Official Implementation of the ECCV'22 paper `Augmenting Deep Classifiers with Polynomial Neural Networks'
This repository contains implementations of Squeeze and excitation networks on ResNet, ResNeXt, and InceptionV3 models. More detail in the report.
The project focuses on classifying brain tumors using the Multi-Modal Squeeze and Excitation Network.
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