Abstract Domain incremental learning aims to adapt to a sequence of domains with access
to only a small subset of data (ie, memory) from previous domains. Various methods have …

Neural collapse (NC) refers to the surprising structure of the last layer of deep neural
networks in the terminal phase of gradient descent training. Recently, an increasing amount …

While overparameterization in machine learning models offers great benefits in terms of
optimization and generalization, it also leads to increased computational requirements as …

Neural collapse provides an elegant mathematical characterization of learned last layer
representations (aka features) and classifier weights in deep classification models. Such …

Modern deep neural networks have achieved impressive performance on tasks from image
classification to natural language processing. Surprisingly, these complex systems with …

As model size continues to grow and access to labeled training data remains limited,
transfer learning has become a popular approach in many scientific and engineering fields …

Visible-infrared object detection has attracted increasing attention recently due to its
superior performance and cost-efficiency. Most existing methods focus on the detection of …

Over the past decade, deep learning has proven to be a highly effective tool for learning
meaningful features from raw data. However, it remains an open question how deep …

Recent studies have noted an intriguing phenomenon termed Neural Collapse that is when
the neural networks establish the right correlation between feature spaces and the training …

Over the past few years, an extensively studied phenomenon in training deep networks is
the implicit bias of gradient descent towards parsimonious solutions. In this work, we …