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Author: Muktabh Mayank Srivastava

Affiliation: ParallelDots Inc, Gurugram, India

Keyword(s): Transfer Learning, Semi Supervised Learning, Few Shot Classification, Retail Product Classification.

Abstract: Retail product Image classification problems are often few shot classification problems, given retail product classes cannot have the type of variations across images like a cat or dog or tree could have. Previous works have shown different methods to finetune Convolutional Neural Networks to achieve better classification accuracy on such datasets. In this work, we try to address the problem statement : Can we pretrain a Convolutional Neural Network backbone which yields good enough representations for retail product images, so that training a simple logistic regression on these representations gives us good classifiers ? We use contrastive learning and pseudolabel based noisy student training to learn representations that get accuracy in order of the effort of finetuning the entire Convnet backbone for retail product image classification.

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Paper citation in several formats:
Srivastava, M. (2022). Using Contrastive Learning and Pseudolabels to Learn Representations for Retail Product Image Classification. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP; ISBN 978-989-758-555-5; ISSN 2184-4321, SciTePress, pages 659-663. DOI: 10.5220/0010911000003124

@conference{visapp22,
author={Muktabh Mayank Srivastava.},
title={Using Contrastive Learning and Pseudolabels to Learn Representations for Retail Product Image Classification},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP},
year={2022},
pages={659-663},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010911000003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP
TI - Using Contrastive Learning and Pseudolabels to Learn Representations for Retail Product Image Classification
SN - 978-989-758-555-5
IS - 2184-4321
AU - Srivastava, M.
PY - 2022
SP - 659
EP - 663
DO - 10.5220/0010911000003124
PB - SciTePress

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