The document discusses techniques for using deep learning with limited data. It presents methods for data synthesis, domain adaptation, and data cleaning. For data synthesis, it describes using a game engine to procedurally generate synthetic videos with automatic annotations for action recognition training. For domain adaptation, it applies a model trained on mouse tracking saliency data to eye tracking data. For data cleaning, it introduces a technique to prune noisy images from a landmark dataset to obtain reliable training annotations. The techniques aim to leverage limited data to train deep networks for tasks like saliency mapping, image retrieval, and action recognition.