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Supervised deep learning models require significant amount of labeled data toachieve an acceptable performance on a specific task. However, when tested onunseen data, the models may not perform well. Therefore, the models need to betrained with additional and varying labeled data to improve the generalization.In this work, our goal is to understand the models, their performance andgeneralization. We establish image-image, dataset-dataset, and image-datasetdistances to gain insights into the model's behavior. Our proposed distancemetric when combined with model performance can help in selecting anappropriate model/architecture from a pool of candidate architectures. We haveshown that the generalization of these models can be improved by only adding asmall number of unseen images (say 1, 3 or 7) into the training set. Ourproposed approach reduces training and annotation costs while providing anestimate of model performance on unseen data in dynamic environments.

Revealing the Underlying Patterns: Investigating Dataset Similarity, Performance, and Generalization