The linear separability problem: some testing methods

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Data-Efficient Image Recognition with Contrastive Predictive Coding (2019, methodology): “Following prior work in self-supervised learning, we re-evaluated the CPC objective in terms of its ability to linearly separate image classes if given all the training labels, which is an informative metric given the connection between linear separability and classiﬁcation complexity [18].”
Matching Recall and Storage in Sequence Learning with Spiking Neural Networks (2013, methodology): “Linear separability, which is the criterion for learnability, was tested with the linear programming method provided by Mathematica (see (Elizondo, 2006) for details).”