Improving Generalization and Robustness in SNNs Through Signed Rate Encoding and Sparse Encoding Attacks
Mukhoty, Bhaskar Alquabeh, Hilal Gu, Bin
Mukhoty, Bhaskar
Alquabeh, Hilal
Gu, Bin
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Department
Machine Learning
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Conference proceeding
Date
2025
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English
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Abstract
Rate-encoded spiking neural networks (SNNs) are known to offer superior adversarial robustness compared to direct-encoded SNNs but have relatively poor generalization on clean input. While the latter offers good generalization on clean input it suffers poor adversarial robustness under standard training. A key reason for this difference is the input noise introduced by the rate encoding, which encodes a pixel intensity with T independent Bernoulli samples. To improve the generalization of rate-encoded SNNs, we propose the signed rate encoding (SRATE) that allows mean centering of the input and helps reduce the randomness introduced by the encoding, resulting in improved clean accuracy. In contrast to rate encoding, where input restricted to [0, 1]d is encoded in {0, 1}d×T, the signed rate encoding allows input in [−1, 1]d to be encoded with spikes in {−1, 0, 1}d×T, where positive (negative) inputs are encoded with positive (negative) spikes. We further construct efficient Sparse Encoding Attack (SEA) on standard and signed rate encoded input, which performs l0-norm restricted adversarial attack in the discrete encoding space. We prove the theoretical optimality of the attack under the first-order approximation of the loss and compare it empirically with the existing attacks on the input space. Adversarial training performed with SEA, under signed rate encoding, offers superior adversarial robustness to the existing attacks and itself. Experiments conducted on standard datasets show the effectiveness of sign rate encoding in improving accuracy across all settings including adversarial robustness. The code is available at https://github.com/BhaskarMukhoty/SignedRateEncoding. © 2025 13th International Conference on Learning Representations, ICLR 2025. All rights reserved.
Citation
B. Mukhoty, H. AlQuabeh, and B. Gu, “Improving Generalization and Robustness in SNNs Through Signed Rate Encoding and Sparse Encoding Attacks,” International Conference on Representation Learning, vol. 2025, pp. 77120–77136, May 2025
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13th International Conference on Learning Representations, ICLR 2025
Conference
13th International Conference on Learning Representations, ICLR 2025
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Source
13th International Conference on Learning Representations, ICLR 2025
Publisher
International Conference on Learning Representations, ICLR