Developing Data-Conscious Deep Learning Models for Product Classification

In online commerce systems that trade in many products, it is important to classify the products accurately according to the product description. As may be expected, the recent advances in deep learning technologies have been applied to automatic product classification. The efficiency of a deep learning model depends on the training data and the appropriateness of the learning model for the data domain. This is also applicable to deep learning models for automatic product classification. In this study, we propose deep learning models that are conscious of input data comprising text-based product information. Our approaches exploit two well-known deep learning models and integrate them with the processes of input data selection, transformation, and filtering. We demonstrate the practicality of these models through experiments using actual product information data. The experimental results show that the models that systematically consider the input data may differ in accuracy by approximately 30% from those that do not. This study indicates that input data should be sufficiently considered in the development of deep learning models for product classification.

Deep Learning for Retail Product Recognition: Challenges and Techniques

Taking time to identify expected products and waiting for the checkout in a retail store are common scenes we all encounter in our daily lives. The realization of automatic product recognition has great significance for both economic and social progress because it is more reliable than manual operation and time-saving. Product recognition via images is a challenging task in the field of computer vision. It receives increasing consideration due to the great application prospect, such as automatic checkout, stock tracking, planogram compliance, and visually impaired assistance. In recent years, deep learning enjoys a flourishing evolution with tremendous achievements in image classification and object detection. This article aims to present a comprehensive literature review of recent research on deep learning-based retail product recognition. More specifically, this paper reviews the key challenges of deep learning for retail product recognition and discusses potential techniques that can be helpful for the research of the topic. Next, we provide the details of public datasets which could be used for deep learning. Finally, we conclude the current progress and point new perspectives to the research of related fields.