Abstract: Long-tailed image classification faces challenges of data imbalance and poor feature representation for tail classes, leading to biased predictions favoring head classes. While most existing ...
Following papers are implemented using PyTorch. ResNeXt-29 8x64d 3.97 (1 run) 3.65 (average of 10 runs) 42h50m* ResNeXt-29 16x64d 3.58 (average of 10 runs) shake-shake-26 2x32d (S-S-I) 3.68 3.55 ...
To build a self-supervised magnetic resonance imaging (MRI) foundation model from routine clinical scans and to test whether it can support key glioma-related applications, including post-therapy ...
Learn how Network in Network (NiN) architectures work and how to implement them using PyTorch. This tutorial covers the concept, benefits, and step-by-step coding examples to help you build better ...
This repository contains Python notebooks demonstrating image classification using Azure AutoML for Images. These notebooks provide practical examples of building computer vision models for various ...
Abstract: Most hyperspectral image (HSI) classification methods assume that all classes in the test set are present during training. However, in real-world applications, acquiring labeled training ...
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