# Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels

## TL;DR

This paper introduces an unsupervised incremental learning approach using confidence-based pseudo-labels, enabling models to learn new classes from unlabeled data while maintaining previous knowledge, with strong results on standard datasets.

## Contribution

The paper proposes a novel unsupervised incremental learning method that replaces human labels with pseudo-labels, integrating confidence-based selection to improve learning from unlabeled data.

## Key findings

- ICPL achieves competitive accuracy with supervised methods.
- Outperforms state-of-the-art class-iNCD methods by over 5%.
- Effective on CIFAR100, ImageNet100, and fine-grained datasets.

## Abstract

Deep learning models have achieved state-of-the-art performance in many computer vision tasks. However, in real-world scenarios, novel classes that were unseen during training often emerge, requiring models to acquire new knowledge incrementally. Class-Incremental Learning (CIL) methods enable a model to learn novel classes while retaining knowledge of previous classes. However, these methods make the strong assumption that the incremental dataset is fully labeled, which is unrealistic in practice. In this work, we propose an unsupervised Incremental Learning method using Confidence-based Pseudo-labels (ICPL), which replaces human annotations with pseudo-labels, enabling incremental learning from unlabeled datasets. We integrate these pseudo-labels into various CIL methods with confidence-based selection and evaluate performance degradation on CIFAR100 and ImageNet100. Then, we compare our approach to popular Class Incremental Novel Category Discovery (class-iNCD) methods addressing similar challenges. Additionally, we apply our method to fine-grained datasets to demonstrate its real-world practicality and measure its computational complexity to validate its suitability for resource-constrained environments. ICPL achieves competitive results compared to supervised methods and outperforms state-of-the-art class-iNCD methods by more than 5% in final accuracy.