Quantization is widely applied in machine learning to reduce computational and storage costs for both data and models. Considering that classification tasks are fundamental to the field, it is crucial to investigate how quantization impacts classification performance. Traditional research has focused on quantization errors, assuming that larger errors generally lead to lower classification accuracy. However, this assumption lacks a solid theoretical foundation and often contradicts empirical observations. For example, despite introducing significant errors, $\{0,1\}$-binary and $\{0, \pm1\}$-ternary quantized data have sometimes achieved classification accuracy comparable or even superior to full-precision data. To reasonably explain this phenomenon, a more accurate evaluation of classification performance is required. To achieve this, we propose a direct analysis of the feature discrimination of quantized data, instead of focusing on quantization errors. Our analysis reveals that both binary and ternary quantization can potentially enhance, rather than degrade, the feature discrimination of the original data. This finding is supported by classification experiments conducted on both synthetic and real data.

Feature discrimination is a crucial aspect of neural network design, as it directly impacts the network's ability to distinguish between classes and generalize across diverse datasets. The accomplishment of achieving high-quality feature representations ensures high intra-class separability and poses one of the most challenging research directions. While conventional deep neural networks (DNNs) rely on complex transformations and very deep networks to come up with meaningful feature representations, they usually require days of training and consume significant energy amounts. To this end, spiking neural networks (SNNs) offer a promising alternative. SNN's ability to capture temporal and spatial dependencies renders them particularly suitable for complex tasks, where multi-modal data are required. In this paper, we propose a feature discrimination approach for multi-modal learning with SNNs, focusing on audio-visual data. We employ deep spiking residual learning for visual modality processing and a simpler yet efficient spiking network for auditory modality processing. Lastly, we deploy a spiking multilayer perceptron for modality fusion. We present our findings and evaluate our approach against similar works in the field of classification challenges. To the best of our knowledge, this is the first work investigating feature discrimination in SNNs.

It is shown that one of which is the capability of employing before CNNs feature extraction, HOG[Surasak et al., 2018] high-frequency components. This paper discusses feature for all frequency components manifest noticeable the frequency bias phenomenon in image classification discrimination between classes. However, after CNNs feature tasks: the high-frequency components are actually extraction, while feature discrimination for the low-and much less exploited than the low-and midfrequency middle-frequency components (left two sub-figures) are enhanced components. We first investigate the frequency due to supervised learning, the high-frequency components bias phenomenon by presenting two observations (right two sub-figures) are considerably inhibited.