DP-Net: Learning Discriminative Parts for image recognition
<|reference_start|>DP-Net: Learning Discriminative Parts for image recognition: This paper presents Discriminative Part Network (DP-Net), a deep architecture with strong interpretation capabilities, which exploits a pretrained Convolutional Neural Network (CNN) combined with a part-based recognition module. This system learns and detects parts in the images that are discriminative among categories, without the need for fine-tuning the CNN, making it more scalable than other part-based models. While part-based approaches naturally offer interpretable representations, we propose explanations at image and category levels and introduce specific constraints on the part learning process to make them more discrimative.<|reference_end|>
@article{sicre2024dp-net:,
    title={DP-Net: Learning Discriminative Parts for image recognition},
    author={Ronan Sicre, Hanwei Zhang, Julien Dejasmin, Chiheb Daaloul, St'ephane
  Ayache, Thierry Arti`eres},
    journal={arXiv preprint arXiv:2404.15037},
    year={2024},
    doi={10.1109/ICIP49359.2023.10222053},
    archivePrefix={arXiv},
    eprint={2404.15037},
    primaryClass={cs.CV}
}