Neural Network Embedding Generation via Triplet Loss Optimization
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Solution Overview
Problem
Existing image processing technologies using neural networks face challenges in generating effective numeric embeddings that distinguish between images of the same object type and different object types, especially under varying imaging conditions, with inefficiencies in training time and accuracy.
Innovation Solution
The method involves training a deep convolutional neural network using triplets of anchor, positive, and negative images to minimize triplet loss, where the neural network generates numeric embeddings for input images, and adjusts its parameters to ensure small distances between embeddings of the same object type and large distances between different object types, optimizing the embedding process directly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional neural networks are used to generate numeric embeddings, then the network can process images, but the embeddings fail to effectively distinguish between images of the same object type and different object types under varying imaging conditions
Solution Approach 1:
The patent changes the training objective parameter from standard classification loss to triplet loss, which directly optimizes the geometric relationships between embeddings. This parameter change enables the network to learn embeddings that maintain consistent discrimination capabilities across varying imaging conditions by explicitly enforcing distance constraints between anchor-positive and anchor-negative pairs.
Solution Approach 2:
The patent transitions from traditional classification-based training to metric learning in the embedding space, adding a geometric dimension to the training objective. By optimizing embeddings in a continuous vector space with explicit distance constraints, the system achieves better discrimination accuracy and reliability across different imaging conditions.
2Manufacturing precision
If existing image processing methods are used, then images can be processed, but training time is excessive and accuracy is insufficient
Solution Approach 1:
The patent performs preliminary action by pre-processing images to generate triplets with explicit positive and negative examples before training. This pre-organization of training data in triplet format enables more efficient learning compared to traditional methods, reducing training time while improving embedding accuracy through direct optimization of discrimination objectives.
Solution Approach 2:
The patent replaces the traditional mechanical training process with gradient-based optimization using triplet loss. This substitution enables more efficient convergence by directly optimizing the embedding space geometry, achieving higher accuracy with reduced training time compared to conventional iterative training approaches.
3Measurement precision
If standard neural network training is applied, then the network can learn from data, but it cannot effectively minimize the distance between embeddings of the same object while maximizing distance between different objects
Solution Approach 1:
The patent introduces triplet loss as an intermediary training objective that mediates between the neural network's parameter updates and the desired embedding relationships. This intermediary loss function explicitly encodes the geometric constraints (minimizing anchor-positive distance, maximizing anchor-negative distance), making the training process more effective while maintaining operational simplicity through automatic differentiation.
Data Source
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AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating numeric embeddings of images. One of the methods includes obtaining training images; generating a plurality of triplets of training images; and training a neural network on each of the triplets to determine trained values of a plurality of parameters of the neural network, wherein training the neural network comprises, for each of the triplets: processing the anchor image in the triplet using the neural network to generate a numeric embedding of the anchor image; processing the positive image in the triplet using the neural network to generate a numeric embedding of the positive image; processing the negative image in the triplet using the neural network to generate a numeric embedding of the negative image; computing a triplet loss; and adjusting the current values of the parameters of the neural network using the triplet loss.