Texture-based latent space disentanglement for enhancement of a training dataset for ANN-based classification of fruit and vegetables
Information Processing in Agriculture
School of Engineering
Edith Cowan University (ECU) / Australia and Higher Education Commission (HEC) Pakistan / The Islamia University of Bahawalpur (IUB) Pakistan (5-1/HRD/UESTPI(Batch-V)/1182/2017/HEC)
The capability of Convolutional Neural Networks (CNNs) for sparse representation has significant application to complex tasks like Representation Learning (RL). However, labelled datasets of sufficient size for learning this representation are not easily obtainable. The unsupervised learning capability of Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) provide a promising solution to this issue through their capacity to learn representations for novel data samples and classification tasks. In this research, a texture-based latent space disentanglement technique is proposed to enhance learning of representations for novel data samples. A comparison is performed among different VAEs and GANs with the proposed approach for synthesis of new data samples. Two different VAE architectures are considered, a single layer dense VAE and a convolution based VAE, to compare the effectiveness of different architectures for learning of the representations. The GANs are selected based on the distance metric for disjoint distribution divergence estimation of complex representation learning tasks. The proposed texture-based disentanglement has been shown to provide a significant improvement for disentangling the process of representation learning by conditioning the random noise and synthesising texture rich images of fruit and vegetables.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Hameed, K., Chai, D., & Rassau, A. (2023). Texture-based latent space disentanglement for enhancement of a training dataset for ANN-based classification of fruit and vegetables. Information Processing in Agriculture, 10(1), 85-105. https://doi.org/10.1016/j.inpa.2021.09.003