# Semi-Supervised Learning Enabled by Multiscale Deep Neural Network Inversion

arXiv: https://arxiv.org/abs/1802.10172 · HTML: https://mlsanity.com/paper/1802.10172/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2018.

## Built on by (most cited first, 1 of 1)

- [Analysis of Machine Learning Approaches to Packing Detection](https://mlsanity.com/paper/2105.00473/) (2021)

## Abstract (arXiv)

Deep Neural Networks (DNNs) provide state-of-the-art solutions in several difficult machine perceptual tasks. However, their performance relies on the availability of a large set of labeled training data, which limits the breadth of their applicability. Hence, there is a need for new em semi-supervised learning methods for DNNs that can leverage both (a small amount of) labeled and unlabeled training data. In this paper, we develop a general loss function enabling DNNs of any topology to be trained in a semi-supervised manner without extra hyper-parameters. As opposed to current semi-supervised techniques based on topology-specific or unstable approaches, ours is both robust and general. We demonstrate that our approach reaches state-of-the-art performance on the SVHN (9.82% test error, with 500 labels and wide Resnet) and CIFAR10 (16.38% test error, with 8000 labels and sigmoid convolutional neural network) data sets.