Researchers have developed a new framework for image classification that addresses the challenges of training on large datasets by efficiently selecting representative subsets of data. The framework, called SCOre-Stratified Selection (SCOSS), partitions data based on scores and samples from each interval, combined with an ensemble aggregation over multiple runs. Experiments show SCOSS is competitive with existing methods, particularly effective for Simple Graph Convolution (SGC) classifiers, and offers favorable trade-offs between accuracy and efficiency, especially when using fewer labeled samples. AI
IMPACT This framework could improve the efficiency of training AI models on large image datasets, potentially reducing computational costs and accelerating development.
RANK_REASON The cluster contains an academic paper detailing a new framework for image classification.
- arXiv
- Image Classification
- Lucas Pascotti Valem
- SCOre-Stratified Selection (SCOSS)
- Simple Graph Convolution (SGC)
- alphaXiv
- CatalyzeX
- DagsHub
- Gotit.pub
- Hugging Face
- ScienceCast
- SCOre-Stratified Selection
- Simple Graph Convolution
- Support Vector Machine
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