A research paper by scientists from the Indian Institute of Science (IISc), Bengaluru, has secured a place among the top 15 submissions at the Computer Vision and Pattern Recognition (CVPR) 2026 conference held in Colorado, US. The paper, titled 'Rethinking Dataset Distillation: Hard Truths about Soft Labels', was authored by Priyam Dey, R Venkatesh Babu, Additya Sahdev, Sunny Bhati and Konda Reddy Mopuri from the IISc Department of Computational and Data Science (CDS). According to the IISc, the paper reached the finals after being shortlisted from nearly 16,000 submissions. The study revisits 'dataset distillation', which seeks to reduce the amount of data required to train AI systems. CDS Head Prof. R Venkatesh Babu stated that carefully selected random samples can perform on par with more complex techniques, saying, "Random (training data) samples also give you the same accuracy." The work has been demonstrated on image classification tasks, such as categorizing nearly a million images into around 1,000 classes, and could be extended to audio. Prof. Babu noted that reducing training data could lower the carbon footprint and infrastructure requirements, including electricity consumption and graphics processing units (GPUs).