Color-Oriented Redundancy Reduction in Dataset Distillation

Dataset Distillation (DD) is designed to generate condensed representations of extensive image datasets, enhancing training efficiency. Despite recent advances, there remains considerable potential for improvement, particularly in addressing the notable redundancy within the color space of distilled images. In this paper, we propose AutoPalette, a framework that minimizes color redundancy at the individual image and overall dataset levels, respectively. At the image level, we employ a palette network, a specialized neural network, to dynamically allocate colors from a reduced color space to each pixel. The palette network identifies essential areas in synthetic images for model training and consequently assigns more unique colors to them. At the dataset level, we develop a color-guided initialization strategy to minimize redundancy among images. Representative images with the least replicated color patterns are selected based on the information gain. A comprehensive performance study involving various datasets and evaluation scenarios is conducted, demonstrating the superior performance of our proposed color-aware DD compared to existing DD methods. The code is available at \url{https://github.com/KeViNYuAn0314/AutoPalette}.

An Efficient Newton Method for Extreme Similarity Learning with Nonlinear Embeddings

We study the problem of learning similarity by using nonlinear embedding models (e.g., neural networks) from all possible pairs. This problem is well-known for its difficulty of training with the extreme number of pairs. Existing optimization methods extended from stochastic gradient methods suffer from slow convergence and high complexity per pass of all possible pairs. Inspired by some recent works reporting that Newton methods are competitive for training certain types of neural networks, in this work, we novelly apply the Newton method for this problem. A prohibitive cost depending on the extreme number of pairs occurs if the Newton method is directly applied. We propose an efficient algorithm which successfully eliminates the cost. Our proposed algorithm can take advantage of second-order information and lower time complexity per pass of all possible pairs. Experiments conducted on large-scale data sets demonstrate that the proposed algorithm is more efficient than existing algorithms.