Compressed Learning in MCA Architectures to Tolerate Malicious Noise. Abstract: It is shown that compressed learning tolerates adversarial attacks effectively and that classification accuracy is impacted minimally when the compression ratio is selected appropriately.
Adversarial attacks introduce small malicious noises that are usually imperceptible to humans but cause the DNNs to produce incorrect results. DNNs are ...
Topics · Adversarial Attacks · Compressed Learning · Compression Ratio · Neural Network Architectures · Malicious Noise · Classification Accuracy · Latency · Memristive ...
Compressed Learning in MCA Architectures to Tolerate Malicious Noise ... TL;DR: Tolerance to adversarial attacks increases when the compressed learning-based ...
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Compressed Learning in MCA Architectures to Tolerate Malicious Noise · B. PaudelS. Tragoudas. Computer Science, Engineering. 2022 IEEE 28th International ...
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Existing MCA-based neural network architectures use high power consuming voltage ... Compressed Learning in MCA Architectures to Tolerate Malicious Noise.
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