Friday 28 March 2025
The quest for efficient deep learning models has led researchers to explore novel techniques for selecting representative subsets of large datasets, known as coresets. In a recent paper, scientists have attempted to adapt a state-of-the-art coreset selection method to multimodal data, specifically focusing on depth prediction tasks.
To better understand this concept, let’s dive into the basics. Traditional deep learning models rely heavily on vast amounts of training data, which can be both time-consuming and computationally expensive to process. Coresets aim to alleviate this issue by identifying a smaller subset of the original dataset that still retains the essential information needed for accurate model performance.
The researchers’ approach builds upon a submodular gain function, which measures the importance of each sample in the dataset based on its ability to maximize retained information. This gain is then used to recursively select samples from the dataset, ensuring a diverse and representative coreset is formed.
In their experiment, the scientists employed the CLEVR dataset, which consists of multimodal inputs (RGB images and semantic masks) and target depth maps from Omnidata. They utilized the MultiMAE backbone, with input and output adapters trained following the original paper’s guidelines. The team then evaluated various coreset selection methods, including concatenation, mean, sum, PCA, and UMAP dimensionality reduction.
The results were underwhelming, to say the least. Coresets failed to deliver significant improvements over random selection, leading to a 50% performance drop compared to using the full dataset. The best results came from applying PCA dimensionality reduction, but even this showed incremental gains.
So what went wrong? It’s likely that the reduced data representativeness led to coresets behaving similarly to random selection, resulting in poor model convergence. The team also found that attempts to use a linear output adapter for bottleneck embeddings failed, emphasizing the importance of the DPT adapter for depth prediction tasks.
This study serves as a reminder that adapting successful techniques from one domain to another is often more challenging than it seems. Multimodal coreset selection remains an open problem, and further exploration is necessary to unlock its potential. For now, researchers will continue to refine their approaches, pushing the boundaries of what’s possible in the realm of efficient deep learning.
In related news, a recent review of multimodal approaches for medical support systems highlights the need for more research in this area.
Cite this article: “Challenges in Multimodal Coreset Selection for Efficient Deep Learning”, The Science Archive, 2025.
Multimodal, Coreset Selection, Deep Learning, Dataset Reduction, Submodular Gain Function, Dimensionality Reduction, Pca, Umap, Dpt Adapter, Medical Support Systems
