Self-supervised learning (SSL) has revolutionized the field of machine learning by allowing models to learn meaningful representations from unlabeled data. One of the key challenges in SSL is feature redundancy, where learned features become correlated and fail to capture diverse information. Feature decorrelation is a crucial technique to overcome this issue, improving the robustness and … Read more
In the era of artificial intelligence and natural language processing (NLP), abstractive summarization plays a crucial role in condensing large amounts of information into concise, readable summaries. However, two critical challenges arise in this process: faithfulness and factuality. Faithfulness refers to how accurately a summary reflects the original content, while factuality ensures that the summary … Read more
Generative Adversarial Networks (GANs) have revolutionized the field of deep learning, enabling the generation of realistic images, videos, and other types of data. However, training GANs is notoriously difficult due to instability and mode collapse. One of the most effective techniques to stabilize GAN training is Spectral Normalization. This topic explores what spectral normalization is, … Read more
Diffusion models have become a crucial tool in various scientific and engineering fields, from biology to physics, and even artificial intelligence. These models describe how substances, information, or even behaviors spread through a medium or population. In the context of artificial intelligence, diffusion models are gaining traction as a method for generating data, particularly in … Read more