Current unsupervised learning methods depend on end-to-end training via deep learning techniques such as self-supervised learning, with high computational requirements, or employ layer-by-layer training using bio-inspired approaches like Hebbian learning, using local learning rules incompatible with supervised learning. Both approaches are problematic for edge AI hardware that relies on sparse computational resources and would strongly benefit from alternating between unsupervised and supervised learning phases - thus leveraging widely available unlabeled data from the environment as well as labeled training datasets. To solve this challenge, in this work, we introduce a 'self-defined target' that uses Winner-Take-All (WTA) selectivity at the network's final layer, complemented by regularization through biologically inspired homeostasis mechanism. This approach, framework-agnostic and compatible with both global (Backpropagation) and local (Equilibrium propagation) learning rules, achieves a 97.6% test accuracy on the MNIST dataset. Furthermore, we demonstrate that incorporating a hidden layer enhances classification accuracy and the quality of learned features across all training methods, showcasing the advantages of end-to-end unsupervised training. Extending to semi-supervised learning, our method dynamically adjusts the target according to data availability, reaching a 96.6% accuracy with just 600 labeled MNIST samples. This result highlights our 'unsupervised target' strategy's efficacy and flexibility in scenarios ranging from abundant to no labeled data availability.

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