Image Classifier Training With Latent Cluster Filtering
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Solution Overview
Problem
Conventional machine learning systems face inefficiencies due to the reliance on full annotated datasets, leading to high computational overhead and domain mismatch errors when adapting to new or shifting target domains.
Innovation Solution
The solution employs latent vector clustering, distributional discrepancy scoring, and similarity-based graph pruning to generate an aligned and refined subset of annotated data, optimizing training for visual models on real-world, unlabeled inputs, thereby reducing annotation overhead and improving model generalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full annotated datasets are used for training, then model training accuracy is improved, but computational overhead and annotation costs increase substantially
Solution Approach 1:
The patent extracts and removes irrelevant or redundant data points from the annotated dataset through filtering mechanisms. By identifying and eliminating data that does not contribute meaningfully to model training, the system reduces computational overhead while preserving the essential information needed for accurate training, thus resolving the contradiction between training accuracy and computational efficiency
Solution Approach 2:
The patent applies different quality standards and filtering criteria to different subsets of data based on their relevance and utility. Rather than uniformly processing all annotated data, the system identifies high-value regions in the data space and focuses computational resources on those areas, improving efficiency while maintaining training accuracy through selective quality assessment
2Reliability
If full annotated datasets are used for training, then model comprehensiveness is improved, but annotation costs and time consumption increase
Solution Approach 1:
The patent performs preliminary filtering and selection of annotated data points before the actual model training process. By pre-identifying and removing irrelevant data through automated criteria, the system reduces the time and resources needed for annotation while ensuring that only high-quality, relevant data proceeds to training, thus maintaining model reliability without proportional increases in annotation effort
Solution Approach 2:
The patent uses unlabeled data to create surrogate representations or proxies that can guide the selection of annotated data. By leveraging patterns from unlabeled data to identify representative annotated samples, the system reduces the need for extensive manual annotation while preserving model generalization capabilities through carefully selected data copies or substitutes
3Adaptability or versatility
If annotated datasets are adapted to new domains, then model adaptability is improved, but domain mismatch errors increase due to data distribution shifts
Solution Approach 1:
The patent implements dynamic filtering criteria that adapt to the characteristics of different target domains. Rather than using static selection rules, the system adjusts its data selection strategy based on the specific domain being targeted, allowing it to maintain high adaptability while minimizing domain mismatch errors through context-aware data selection
Solution Approach 2:
The patent modifies key parameters of the data selection process, such as similarity thresholds and relevance criteria, based on the target domain characteristics. By changing these parameters dynamically according to the domain being adapted to, the system achieves better domain adaptability while reducing domain mismatch errors through parameter optimization
Data Source
AI summary
An example operation may include at least one of converting an annotated dataset loaded from a storage into a first set of latents, converting a non-annotated dataset loaded from the storage into a second set of latents creating an aligned subset of data from the annotated dataset comprising: clustering the first set of latents into a plurality of clusters, determining a discrepancy score for each cluster in the plurality of clusters and the second set of latents, creating a refined subset of data from the annotated dataset by including at least one data from each cluster of the plurality of clusters, wherein adding the at least one data lowers the discrepancy score of the refined subset of data and the second set of latents, determining a similarity score between latents in the first set of latents and the second set of latents, wherein the aligned subset of data is created from the annotated dataset by parsing the refined subset into pairs of latents and for each of the pairs of latents, including a latent with a highest similarity score, and training an image classification model using the aligned subset, the image classification model configured to classify image data received from a user device.