ChihebTrabelsi/deep_complex_networks
Implementation related to the Deep Complex Networks
This project provides code to explore and compare how different deep learning architectures, specifically those using 'complex-valued' numbers, perform on tasks like image classification and music analysis. It takes in image datasets (like CIFAR-10) or audio datasets (like MusicNet) and outputs performance metrics and visualizations, demonstrating the effectiveness of complex networks. This is intended for machine learning researchers and practitioners experimenting with novel neural network designs for specific data types.
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Use this if you are a machine learning researcher interested in understanding or reproducing experiments with complex-valued neural networks for computer vision or audio processing.
Not ideal if you are looking for a pre-trained model or a plug-and-play solution for general image or audio classification without delving into network architecture research.
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782
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Language
Python
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MIT
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Last pushed
Jan 16, 2019
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