Learning with limited data and its applications on video understanding

However, limited data poses a significant challenge due to the increasing complexity of tasks and the growing size of neural networks which require larger datasets for training. In this thesis, we examine the problem of limited sample learning, which aims to improve deep neural network performance when given a limited number of samples. <br><br>Chapter 3 focuses on learning with limited samples using generative models.

We propose an improved version of the variational auto-encoder (VAE) using normalising flow (NF). We demonstrate that by replacing the Gaussian prior in the VAE with NF, overfitting in the low data regime can be reduced.<br><br> In Chapter 4, we present an extended version of FlowReg, which aims to regularise the training process by transferring knowledge from one or more related domains to the target domain. We propose a cross-domain flow to link the source domain and the target domain, which aligns the posterior distributions in multi-domain learning.