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NucNormZSL: nuclear norm-based domain adaptation in zero-shot learning

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Abstract

The ability of human beings to recognize novel concepts has attracted significant attention in the research community. Zero-shot learning, also known as zero-data learning, seeks to build models that can recognize novel class instances even without “seeing” them during their training; some description of novel classes is, however, required. In this work, we pose zero-shot learning as a dictionary learning problem to learn the projection functions from feature to semantic space as dictionaries in the source and target domains. To get a robust projection mapping in the source domain, we introduce nuclear norm to achieve low-rank solutions. Further, this low-ranked dictionary is used as a regularizer in the target domain so that the knowledge contained in the source dictionary is utilized in the target domain. In our experiments, source domain contains the seen class images, their ground truths and attribute representations while corresponding data for unseen class are contained in the target domain. We also use label propagation as an alternative to the nearest neighbor search in the semantic space for class-label assignment. Our proposed model, NucNormZSL, achieves state-of-the-art results for the Large Attribute (LAD) dataset and remains fairly competitive with existing approaches on Animals with Attributes-2 (AWA2), Caltech-UCSD Birds (CUB) and SUN datasets in the conventional setting and generalized setting.

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Funding

We declare that our work is funded by Indian Institute of Information Technology Allahabad.

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Authors and Affiliations

  1. Indian Institute of Information Technology Allahabad, Prayagraj, Uttar Pradesh, India

    Upendra Pratap Singh & Krishna Pratap Singh

  2. Indian Institute of Technology Mandi, Suran, Himachal Pradesh, India

    Manoj Thakur

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  1. Upendra Pratap Singh
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Correspondence to Upendra Pratap Singh.

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Singh, U.P., Singh, K.P. & Thakur, M. NucNormZSL: nuclear norm-based domain adaptation in zero-shot learning. Neural Comput & Applic 34, 2353–2374 (2022). https://doi.org/10.1007/s00521-021-06461-1

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