  Quantum entanglement is a fundamental property commonly used in various
quantum information protocols and algorithms. Nonetheless, the problem of
identifying entanglement has still not reached a general solution for systems
larger than $2\times3$. In this study, we use deep convolutional NNs, a type of
supervised machine learning, to identify quantum entanglement for any
bipartition in a 3-qubit system. We demonstrate that training the model on
synthetically generated datasets of random density matrices excluding
challenging positive-under-partial-transposition entangled states (PPTES),
which cannot be identified (and correctly labeled) in general, leads to good
model accuracy even for PPTES states, that were outside the training data. Our
aim is to enhance the model's generalization on PPTES. By applying
entanglement-preserving symmetry operations through a triple Siamese network
trained in a semi-supervised manner, we improve the model's accuracy and
ability to recognize PPTES. Moreover, by constructing an ensemble of Siamese
models, even better generalization is observed, in analogy with the idea of
finding separate types of entanglement witnesses for different classes of
states.
