Two-Stage Neural Network for Image Classification Accuracy
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Solution Overview
Problem
Current image classification methods face challenges in efficiently training classification models to accurately distinguish between similar and dissimilar images, leading to suboptimal performance in target recognition and classification tasks.
Innovation Solution
A method involving the training of a first sub-network using positive and negative samples, where the initial weights of a second sub-network are used to construct a classification model, allowing for effective feature extraction and improved target recognition by utilizing a second image set with common images, and employing loss functions like Npairs Loss, Triplet Loss, or Contrastive Loss to enhance inter-class differences and reduce intra-class variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current image classification methods are used, then training can be performed with standard approaches, but the accuracy of target recognition is insufficient and the training process is inefficient
Solution Approach 1:
The method performs preliminary feature extraction training using a first image set and first sub-network before the main classification training. This preliminary action pre-processes the feature extraction capabilities, allowing the second sub-network to start with pre-trained weights, thereby improving both recognition accuracy and training efficiency by avoiding redundant learning in the main training phase
Solution Approach 2:
The training process is segmented into two distinct phases: (1) feature extraction training using a first sub-network with positive and negative samples, and (2) classification training using a second sub-network. This segmentation allows each phase to focus on specific learning objectives, improving overall training efficiency and recognition accuracy by breaking down the complex training task into manageable stages
2Manufacturing precision
If standard training approaches are used, then the training process can be straightforward, but the feature distance between similar images is not sufficiently reduced and the feature distance between dissimilar images is not sufficiently increased
Solution Approach 1:
The first sub-network acts as an intermediary that processes positive and negative samples to generate pre-trained weights. These weights serve as a bridge between raw image data and the final classification model, enabling the second sub-network to achieve better feature discrimination without directly processing all training complexities, thus improving feature distance discrimination while managing complexity
Solution Approach 2:
The method changes the parameters (weights) of the second sub-network by initializing them with weights from the trained first sub-network. This parameter transfer allows the classification model to inherit learned feature representations, improving the feature distance discrimination between similar and dissimilar images while avoiding the need to retrain from scratch
Data Source
AI summary
Methods, apparatuses, electronic devices, and mediums related to classification models are provided. The method may include: obtaining a set of positive samples and a set of negative samples, a sample in the set of positive samples including a pair of images from a first image set annotated as similar with each other, and a sample in the set of negative samples including a pair of images annotated as dissimilar with each other from the first image set; training a first sub-network based on the set of positive samples and the set of negative samples; constructing a classification model including a second sub-network, where initial weights of the second sub-network are weights of corresponding nodes of the trained first sub-network; and training the classification model using a second image set that includes at least one image that is common with at least one image in the first image set.