# Zero-sample surface defect detection and classification based on semantic feedback neural network

arXiv: https://arxiv.org/abs/2106.07959 · HTML: https://mlsanity.com/paper/2106.07959/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 51 references that are in the map.
- First posted on arXiv 15 Jun 2021.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly](https://mlsanity.com/paper/1707.00600/) (2017)
- [Bag of Tricks for Image Classification with Convolutional Neural Networks](https://mlsanity.com/paper/1812.01187/) (2018)
- [Evaluation of Output Embeddings for Fine-Grained Image Classification](https://mlsanity.com/paper/1409.8403/) (2014)
- [Semantic Autoencoder for Zero-Shot Learning](https://mlsanity.com/paper/1704.08345/) (2017)
- [Transductive Multi-view Zero-Shot Learning](https://mlsanity.com/paper/1501.04560/) (2015)
- [Transductive Unbiased Embedding for Zero-Shot Learning](https://mlsanity.com/paper/1803.11320/) (2018)
- [Preserving Semantic Relations for Zero-Shot Learning](https://mlsanity.com/paper/1803.03049/) (2018)
- [Zero-Shot Detection](https://mlsanity.com/paper/1803.07113/) (2018)
- [Progressive Ensemble Networks for Zero-Shot Recognition](https://mlsanity.com/paper/1805.07473/) (2018)
- [Zero-Shot Learning and its Applications from Autonomous Vehicles to COVID-19 Diagnosis: A Review](https://mlsanity.com/paper/2004.14143/) (2020)
- [Towards Effective Deep Embedding for Zero-Shot Learning](https://mlsanity.com/paper/1808.10075/) (2018)
- [Learning Class Prototypes via Structure Alignment for Zero-Shot Recognition](https://mlsanity.com/paper/1807.09123/) (2018)
- [A Generative Framework for Zero-Shot Learning with Adversarial Domain Adaptation](https://mlsanity.com/paper/1906.03038/) (2019)

## Abstract (arXiv)

Defect detection and classification technology has changed from traditional artificial visual inspection to current intelligent automated inspection, but most of the current defect detection methods are training related detection models based on a data-driven approach, taking into account the difficulty of collecting some sample data in the industrial field. We apply zero-shot learning technology to the industrial field. Aiming at the problem of the existing "Latent Feature Guide Attribute Attention" (LFGAA) zero-shot image classification network, the output latent attributes and artificially defined attributes are different in the semantic space, which leads to the problem of model performance degradation, proposed an LGFAA network based on semantic feedback, and improved model performance by constructing semantic embedded modules and feedback mechanisms. At the same time, for the common domain shift problem in zero-shot learning, based on the idea of co-training algorithm using the difference information between different views of data to learn from each other, we propose an Ensemble Co-training algorithm, which adaptively reduces the prediction error in image tag embedding from multiple angles. Various experiments conducted on the zero-shot dataset and the cylinder liner dataset in the industrial field provide competitive results.