TinyImagenet Benchmark

TinyImageNet is a benchmark dataset used to evaluate the performance of machine learning models, particularly in scenarios with limited data. Current research focuses on improving data efficiency through techniques like dataset distillation, employing Bayesian frameworks for optimal condensation, and exploring novel loss functions for metric learning in few-shot classification settings. These efforts aim to address challenges such as high computational costs and the need for robust models in resource-constrained environments, ultimately advancing the development of more efficient and effective image classification algorithms. The findings contribute to a broader understanding of representation learning and its limitations, impacting both theoretical advancements and practical applications in areas like computer vision and continual learning.