ML Object Matching Model Triplet Loss Training
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine learning models for object matching in e-commerce struggle with generalizability due to shared contextual elements from the same original image, leading to reduced performance and increased chance of overfitting, especially when objects are visually different but complement each other.
Innovation Solution
The approach modifies triplet loss training by substituting the anchor and positive images with visually similar images not from the original image, using a similarity machine learning model to generate these images, which helps reduce the impact of contextual elements and enhances model generalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If triplet loss training uses anchor and positive images from the same original image, then the model learns contextual relationships, but the model suffers from overfitting and reduced generalizability
Solution Approach 1:
The patent extracts and removes contextual elements from images by using background removal techniques to create isolated object images. This extraction process separates the target objects from their original contextual environments, preventing the model from learning spurious contextual correlations while maintaining focus on object appearance and matching features.
Solution Approach 2:
The patent introduces an intermediary processing step using a neural network to generate modified images with altered contextual elements. This intermediary transformation creates a bridge between original images and training inputs, allowing the model to learn object matching without being biased by specific contextual backgrounds, lighting conditions, or environmental factors.
2Measurement precision
If the model is trained to recognize visually similar objects, then matching accuracy improves, but the model fails to identify visually different but complementary objects
Solution Approach 1:
The patent transforms images into different parameter spaces by converting visual data into embedding vectors through neural network processing. This parameter transformation allows the model to capture semantic relationships and complementary patterns that are not apparent in raw pixel space, enabling accurate matching of visually diverse objects based on their functional and aesthetic compatibility.
Solution Approach 2:
The patent moves the matching problem from two-dimensional image space to high-dimensional embedding space. By projecting images into a multi-dimensional vector space where complementary objects are positioned closer together, the model can identify matching relationships that transcend visual similarity, capturing nuanced patterns of complementarity across different object types and appearances.
3Loss of time
If real-time matching is implemented with small time windows, then user experience improves, but computational complexity increases
Solution Approach 1:
The patent performs preliminary processing by pre-computing and storing embedding vectors for objects in the catalog during offline operations. This advance preparation allows the system to quickly retrieve and compare pre-generated embeddings during real-time user interactions, significantly reducing online computational requirements and enabling fast matching within tight time windows while maintaining high accuracy.
Data Source
AI summary
A computer application may aim to identify first and second “matching” objects. The matching method cannot necessarily be based on how visually similar the two objects are to each other because two matching objects might be different and/or be visually different. Moreover, the images of the objects to be matched might not necessarily have metadata to assist in the matching. In some embodiments, a machine learning model may be trained using a set of digital images, each including two or more matching objects. Triplet loss training may be used, and each triplet may include: an image of a first object extracted from a first image, an image of an object that is visually similar to an image of a second object extracted from the first image, and an image of a third object extracted from a different image.