PMTL: A Progressive Multi-Level Training Framework for Retail Taxonomy Classification

Retail taxonomy classification provides hierarchical labelling of items and it has widespread applications, ranging from product on-boarding, product arrangement and faster retrieval. It is fundamental to both physical space as well as e-commerce. Manual processing based on meta-data was adopted and more recently, image based approaches have emerged. Traditionally, hierarchical classification in retail domain is performed using feature extractors and using different classifier branches for different levels. There are two challenges with this approach: error propagation from previous levels which affects the decision making of the model and the label inconsistency within levels creating unlikely taxonomy tree. Further, the training frameworks rely on large datasets for generalized performance. To address these challenges, we propose PMTL, a progressive multi-level training framework with logit-masking strategy for retail taxonomy classification. PMTL employs a level-wise training framework using cumulative global representation to enhance and generalize output at every level and minimize error propagation. Also, we have proposed logit masking strategy to mask all irrelevant logits of a level and enforce the model to train using only the relevant logits, thereby minimizing label inconsistency. Further, PMTL is a generalized framework that can be employed to any full-shot and few-shot learning scheme without bells and whistles. Our experiments with three datasets with varied complexity in full-shot and few-shot scenario demonstrates the effectiveness of our proposed method compared to the state-of-the-art.