End-to-end phase-mask optimization improves classification under constrained detector readout by increasing class separability but yields no benefit under full readout, where a conventional lens approaches the mutual-information ceiling.
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A hybrid deep learning plus classical ML pipeline for waste image classification reaches up to 100% accuracy on TrashNet and a corrected household dataset while cutting feature dimensionality by over 95%.
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End-to-End Optimization of Incoherent Imaging for Classification Under Detector-Limited Readout
End-to-end phase-mask optimization improves classification under constrained detector readout by increasing class separability but yields no benefit under full readout, where a conventional lens approaches the mutual-information ceiling.
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Towards Accurate and Efficient Waste Image Classification: A Hybrid Deep Learning and Machine Learning Approach
A hybrid deep learning plus classical ML pipeline for waste image classification reaches up to 100% accuracy on TrashNet and a corrected household dataset while cutting feature dimensionality by over 95%.