Distributed Convolutional Neural Networks for Object Recognition

2026-03-10

AI Summary

This work addresses the challenge of object detection in complex backgrounds using only positive samples by proposing a Distributed Convolutional Neural Network (DisCNN). The method employs a tailored loss function that maps positive-class features to a compact region in a high-dimensional space while collapsing negative-class features toward the origin, thereby achieving complete disentanglement between positive and negative representations. Notably, DisCNN eliminates the need for explicit negative-class modeling, substantially reducing model complexity and enabling, for the first time, purely positive-sample-driven object detection. Experimental results demonstrate that DisCNN maintains a lightweight architecture while exhibiting strong generalization capabilities on both seen and unseen positive-class objects, with particularly superior performance in cluttered and complex scenes.

Technology Category

• Computer Vision: Object Detection & Categorization
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Distributed Convolutional Neural Networks
• Object Recognition
• Positive Class Recognition
• Feature Disentanglement
• One-Class Classification

Innovation

• Distributed CNN
• Positive-class recognition
• Feature disentanglement
• Compact embedding
• One-class learning