Instance-level image classification tasks have traditionally relied on
single-instance labels to train models, e.g., few-shot learning and transfer
learning. However, set-level coarse-grained labels that capture relationships
among instances can provide richer information in real-world scenarios. In this
paper, we present a novel approach to enhance instance-level image
classification by leveraging set-level labels. We provide a theoretical
analysis of the proposed method, including recognition conditions for fast
excess risk rate, shedding light on the theoretical foundations of our
approach. We conducted experiments on two distinct categories of datasets:
natural image datasets and histopathology image datasets. Our experimental
results demonstrate the effectiveness of our approach, showcasing improved
classification performance compared to traditional single-instance label-based
methods. Notably, our algorithm achieves 13% improvement in classification
accuracy compared to the strongest baseline on the histopathology image
classification benchmarks. Importantly, our experimental findings align with
the theoretical analysis, reinforcing the robustness and reliability of our
proposed method. This work bridges the gap between instance-level and set-level
image classification, offering a promising avenue for advancing the
capabilities of image classification models with set-level coarse-grained
labels