Deep learning for vision and representation learning

This chapter provides the fundamental concepts of Convolutional Neural Networks (CNNs), a deep neural network widely used in computer vision tasks for processing visual data such as images and videos. Convolutional layers are the building blocks of CNNs, extracting features from the input data and can be used in various configurations to learn increasingly complex representations of the data. Important concepts related to CNNs, including pooling, dilated, and transposed convolutions were discussed here. Pooling is used to reduce the dimensionality of the feature maps, which can help to make the network more efficient. Dilated convolutions increase the receptive field of a convolutional layer, allowing the network to capture features over a larger area and transposed convolutions are used in some neural networks to reverse the downsampling operations performed by the convolutional layers and can be used, for example, to increase the resolution of an image. This chapter also introduces various representation learning techniques that contribute to uncovering underlying latent patterns and trends in data using unsupervised or self-supervised methods, thereby improving the learning of multiple tasks. The techniques covered include autoencoders, denoising autoencoders, variational autoencoders (VAEs), and multi-channel VAEs. VAEs allow for the generation of new samples from the learned distribution parameterized by the mean and standard deviation, while multi-channel VAEs enable the joint analysis of heterogeneous data and enforce latent representations from each data type to match a common target distribution. Additionally, we present the framework, loss functions, and interpretation of contrastive learning. We delve into two prominent algorithms: the Simple Framework for Contrastive Learning of Visual Representations (SimCLR) and Bootstrap Your Own Latent (BYOL). These algorithms offer valuable insights into the field of contrastive learning. Overall, this chapter provides a comprehensive overview of the fundamental concepts and structures of CNNs and deep representation learning techniques that lays the groundwork for more advanced topics such as image segmentation, registration, and generative models.