# PEPL: Precision-Enhanced Pseudo-Labeling for Fine-Grained Image Classification in Semi-Supervised Learning Source: [PEPL: Precision-Enhanced Pseudo-Labeling for Fine-Grained Image Classification in Semi-Supervised Learning]() Collection index: [llms.txt]() Data updated: 2026-05-21T05:00:13.694Z Authors: Bowen Tian, Songning Lai, Lujundong Li, Zhihao Shuai, Runwei Guan, Tian Wu, Yutao Yue Published: 2024-09-05T02:32:07.000Z ## Abstract Fine-grained image classification has witnessed significant advancements with the advent of deep learning and computer vision technologies. However, the scarcity of detailed annotations remains a major challenge, especially in scenarios where obtaining high-quality labeled data is costly or time-consuming. To address this limitation, we introduce Precision-Enhanced Pseudo-Labeling(PEPL) approach specifically designed for fine-grained image classification within a semi-supervised learning framework. Our method leverages the abundance of unlabeled data by generating high-quality pseudo-labels that are progressively refined through two key phases: initial pseudo-label generation and semantic-mixed pseudo-label generation. These phases utilize Class Activation Maps (CAMs) to accurately estimate the semantic content and generate refined labels that capture the essential details necessary for fine-grained classification. By focusing on semantic-level information, our approach effectively addresses the limitations of standard data augmentation and image-mixing techniques in preserving critical fine-grained features. We achieve state-of-the-art performance on benchmark datasets, demonstrating significant improvements over existing semi-supervised strategies, with notable boosts in accuracy and robustness. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Fine-grained Image Classification on CUB200 2011 (test)](): Fine-grained Image Classification; CUB200 2011 (test); Accuracy: 88.75. - [Fine-grained Image Classification on Stanford Cars (test)](): Fine-grained Image Classification; Stanford Cars (test); Accuracy: 91.09. ## Original sources and implementation [Original paper]() [PDF]()