UNSUPERVISED META-LEARNING THROUGH LATENT-SPACE INTERPOLATION IN GENERATIVE MODELS

Abstract

Several recently proposed unsupervised meta-learning approaches rely on synthetic meta-tasks created using techniques such as random selection, clustering and/or augmentation. In this work, we describe a novel approach that generates metatasks using generative models. The proposed family of algorithms generate pairs of in-class and out-of-class samples from the latent space in a principled way, allowing us to create synthetic classes forming the training and validation data of a meta-task. We find that the proposed approach, LAtent Space Interpolation Unsupervised Meta-learning (LASIUM), outperforms or is competitive with current unsupervised learning baselines on few-shot classification tasks on the most widely used benchmark datasets.

1. INTRODUCTION

Few-shot meta-learning algorithms for neural networks such as Mishra et al. (2018); Finn et al. (2017) ; Snell et al. (2017) prepare networks to quickly adapt to unseen tasks. This is done in a meta-training phase that typically involves a large number of supervised learning tasks. Generating supervised tasks depends on large, labeled datasets and hand-specified task distribution. Very recently, several approaches had been proposed that perform the meta-training by generating synthetic training tasks from an unsupervised dataset. This requires us to generate samples with specific pairwise information: in-class pairs of samples that are with high likelihood in the same class, and out-of-class pairs that are with high likelihood not in the same class. In this paper, we rely on recent advances in the field of generative models, such as the variants of generative adversarial networks (GANs) and variational autoencoders (VAEs), to generate the in-class and out-of-class pairs of meta-training data. The fundamental idea of our approach is that in-class pairs are close while out-of-class pairs are far away in the latent space representation of the generative model. Thus, we can generate in-class pairs by interpolating between two out-of-class samples in the latent space and choosing interpolation ratios that put the new sample close to one of the objects. From this latent sample, the generative model creates the new in-class object. Our approach requires little domain-specific tweaking, and the necessary tweaks are human-comprehensible. For instance, we need to choose thresholds for latent space distance that ensure that classes are in different domains, as well as interpolation ratio thresholds that ensure that the sample is in the same class as the nearest edge. Another advantage of the approach is that we can utilize off-the-shelf, pre-trained generative models. The main contributions of this paper can be summarized as follows:



For instance, UMTRA by Khodadadeh et al. (2019) and AAL by Antoniou & Storkey (2019) achieve this through random selection from a domain with many classes for out-of-class pairs and by augmentation for in-class pairs. Hsu et al. (2019) propose CACTUs that creates synthetic labels through unsupervised clustering of the domain.

