Using Contrastive Learning and Pseudolabels to learn representations for Retail Product Image Classification

TL;DR

This paper explores using contrastive learning and pseudolabels to pretrain CNNs for retail product image classification, aiming to produce effective representations that enable simple classifiers to perform well with minimal fine-tuning.

Contribution

It introduces a novel approach combining contrastive learning and pseudolabels for pretraining CNNs specifically for retail product images, reducing the need for extensive fine-tuning.

Findings

Pretrained CNN representations enable simple logistic regression classifiers to achieve high accuracy.

Contrastive learning combined with pseudolabels improves representation quality for retail images.

The method reduces the need for large labeled datasets and extensive fine-tuning.

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 finetuning the entire Convnet backbone for retail product image classification.