A Bag-of-Prototypes Representation for Dataset-Level Applications

• URL: http://arxiv.org/abs/2303.13251v1
• Date: Thu, 23 Mar 2023 13:33:58 GMT
• Title: A Bag-of-Prototypes Representation for Dataset-Level Applications
• Authors: Weijie Tu, Weijian Deng, Tom Gedeon and Liang Zheng
• Abstract summary: This work investigates dataset vectorization for two dataset-level tasks: assessing training set suitability and test set difficulty. We propose a bag-of-prototypes (BoP) dataset representation that extends the image-level bag consisting of patch descriptors to dataset-level bag consisting of semantic prototypes. BoP consistently shows its advantage over existing representations on a series of benchmarks for two dataset-level tasks.
• Score: 24.629132557336312
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work investigates dataset vectorization for two dataset-level tasks: assessing training set suitability and test set difficulty. The former measures how suitable a training set is for a target domain, while the latter studies how challenging a test set is for a learned model. Central to the two tasks is measuring the underlying relationship between datasets. This needs a desirable dataset vectorization scheme, which should preserve as much discriminative dataset information as possible so that the distance between the resulting dataset vectors can reflect dataset-to-dataset similarity. To this end, we propose a bag-of-prototypes (BoP) dataset representation that extends the image-level bag consisting of patch descriptors to dataset-level bag consisting of semantic prototypes. Specifically, we develop a codebook consisting of K prototypes clustered from a reference dataset. Given a dataset to be encoded, we quantize each of its image features to a certain prototype in the codebook and obtain a K-dimensional histogram. Without assuming access to dataset labels, the BoP representation provides a rich characterization of the dataset semantic distribution. Furthermore, BoP representations cooperate well with Jensen-Shannon divergence for measuring dataset-to-dataset similarity. Although very simple, BoP consistently shows its advantage over existing representations on a series of benchmarks for two dataset-level tasks.

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