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One way to combine ICNN and Softmax is to use ICNN as a feature learning module followed by a Softmax layer for classification. The ICNN learns convex features from the input data, and these features are then fed into a Softmax layer to produce class probabilities. This approach can be beneficial when you want to ensure that the features learned by the network have certain convexity properties. The ICNN part ensures robust and stable feature extraction, while the Softmax layer handles the final classification step with probabilistic outputs.
