Computer Science > Computer Vision and Pattern Recognition
arXiv:2603.09220 (cs)
[Submitted on 10 Mar 2026]
Title:Distributed Convolutional Neural Networks for Object Recognition
Authors:Liang Sun
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Abstract:This paper proposes a novel loss function for training a distributed convolutional neural network (DisCNN) to recognize only a specific positive class. By mapping positive samples to a compact set in high-dimensional space and negative samples to Origin, the DisCNN extracts only the features of the positive class. An experiment is given to prove this. Thus, the features of the positive class are disentangled from those of the negative classes. The model has a lightweight architecture because only a few positive-class features need to be extracted. The model demonstrates excellent generalization on the test data and remains effective even for unseen classes. Finally, using DisCNN, object detection of positive samples embedded in a large and complex background is straightforward.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2603.09220 [cs.CV] |
| (or arXiv:2603.09220v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09220
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View a PDF of the paper titled Distributed Convolutional Neural Networks for Object Recognition, by Liang Sun
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