On the effectiveness of ß-VAEs for imageclassification and clustering : Using a disentangled representation for Transfer Learning and Semi-Supervised Learning

Abstract: Data labeling is a critical and costly process, thus accessing large amounts of labeled data is not always feasible. Transfer Learning (TL) and Semi-Supervised Learning (SSL) are two promising approaches to leverage both labeled and unlabeled samples. In this work, we first study TL methods based on unsupervised pre-training strategies with Autoencoder (AE) networks. Then, we focus on clustering in the Semi-Supervised scenario. Previous works introduced the β-VAE, an AE that learns a disentangled data representation from the unlabeled samples. We conduct an initial study of un- supervised pre-training with AEs to assess its impact on image classification tasks. We also design a new training method for the β-VAE based on cyclical annealing. The results show that annealing β during pre-training favours the learning of the target task. However, the best results on the target classification problem are obtained with a ResNet architecture with random initialization, trained only on labeled samples. Empirical evidence suggests that a deep network designed to learn complex patterns can achieve better results than a simpler pre-trained one. It is known that the quality of the data representation also affects the clustering algorithms. Deep Clustering leverages the strengths of Deep Learning to find the representation that better supports clustering. Hence, we introduce the β- VAE with cyclical annealing in the training process of several methods based on Deep Clustering. With respect to a Denoising Autoencoder (DAE), the β-VAE with annealing increases the Clustering Accuracy of the Deep Embedded Clustering (DEC) algorithm of 1% in the unsupervised scenario for the CIFAR-10 dataset. A new learning approach is also designed for clustering in the Semi-Supervised setting. We add an auxiliary supervised fine-tuning phase on the labeled samples. If 20% of the available examples are labeled, and the auxiliary task is executed, the Clustering Accuracy improves of 3.5% when the DAE is replaced by the β-VAE on the Fashion-MNIST dataset. Experiments also show improvements over previous works in the literature.