A dataset-property-based difficulty metric speeds up model selection 6-29 times for few-class tasks and enables smaller models with comparable accuracy.
Image similarity using Deep CNN and Curriculum Learning
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abstract
Image similarity involves fetching similar looking images given a reference image. Our solution called SimNet, is a deep siamese network which is trained on pairs of positive and negative images using a novel online pair mining strategy inspired by Curriculum learning. We also created a multi-scale CNN, where the final image embedding is a joint representation of top as well as lower layer embedding's. We go on to show that this multi-scale siamese network is better at capturing fine grained image similarities than traditional CNN's.
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2026 1verdicts
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Efficient Neural Network Model Selection for Few-Class Application Datasets
A dataset-property-based difficulty metric speeds up model selection 6-29 times for few-class tasks and enables smaller models with comparable accuracy.