Network Inversion of Binarised Neural Nets

arXiv:2402.11995v1 Announce Type: new
Abstract: While the deployment of neural networks, yielding impressive results, becomes more prevalent in various applications, their interpretability and understanding remain a critical challenge. Network inversion, a technique that aims to reconstruct the input space from the model's learned internal representations, plays a pivotal role in unraveling the black-box nature of input to output mappings in neural networks. In safety-critical scenarios, where model outputs may influence pivotal decisions, the integrity of the corresponding input space is …

abstract applications arxiv box challenge cs.lg deployment interpretability nature network networks neural nets neural networks pivotal role space type understanding