Autoencoder Edge Case Classification via Common Loss Function
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Solution Overview
Problem
Machine learning models face challenges in accurately classifying 'edge cases' due to their similarity to multiple classes, leading to overtraining and reduced generalization, making the classification process time-consuming and computationally complex.
Innovation Solution
The use of multiple autoencoders, trained and fine-tuned with a common loss function to differentiate between classes, allowing for accurate classification of edge cases without overtraining by maximizing the difference in reconstruction errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the classification model is over-trained to give higher probability for edge cases, then the classification accuracy for edge cases improves, but the training time and computational complexity increase
Solution Approach 1:
The patent divides the classification task into multiple independent autoencoder models, each specialized for a specific class. Instead of training a single model to handle all classes including edge cases, each autoencoder focuses on learning the characteristics of its designated class, which reduces the training complexity and time required while maintaining high accuracy for edge case classification.
Solution Approach 2:
The patent introduces unlabeled training data as an intermediary element in the fine-tuning process. This unlabeled data serves as a mediator that helps the autoencoders differentiate between classes without requiring additional labeled examples, thereby improving edge case classification accuracy without increasing training time or computational complexity.
2Measurement precision
If the classification model is over-trained to classify edge cases accurately, then the classification accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent segments the classification function across multiple specialized autoencoder models rather than using a single complex model. Each autoencoder is trained independently on specific class data, which simplifies the computational architecture and reduces overall computational complexity while achieving high accuracy for edge cases through the collective decision-making of these specialized models.
Solution Approach 2:
The patent changes the training parameters and approach by using a common loss function that combines reconstruction errors from multiple autoencoders. This parameter change enables the model to achieve high classification accuracy for edge cases without increasing computational complexity, as the loss function efficiently aggregates information from all autoencoders in a computationally efficient manner.
3Measurement precision
If multiple autoencoders are trained and fine-tuned with a common loss function, then the classification accuracy for edge cases improves, but the training process becomes more complex
Solution Approach 1:
The patent merges the training processes of multiple autoencoders by using a common loss function that combines the reconstruction errors from all autoencoders. This merging approach simplifies the training process complexity by providing a unified objective function that guides all autoencoders to learn complementary class characteristics, thereby improving edge case classification accuracy without significantly increasing training process complexity.
Solution Approach 2:
The patent creates a universal training framework where the common loss function serves multiple purposes: it trains all autoencoders simultaneously, enables them to learn from both labeled and unlabeled data, and facilitates their collaboration in classifying edge cases. This multi-functionality reduces the overall complexity of the training process while achieving high classification accuracy.
Data Source
AI summary
Embodiments provide electronic methods and systems for improving edge case classifications. The method performed by a server system includes accessing an input sample dataset including first labeled training data associated with a first class, and second labeled training data associated with a second class, from a database. Method includes executing training of a first autoencoder and a second autoencoder based on the first and second labeled training data, respectively. Method includes providing the first and second labeled training data along with unlabeled training data accessed from the database to the first and second autoencoders. Method includes calculating a common loss function based on a combination of a first reconstruction error associated with the first autoencoder and a second reconstruction error associated with the second autoencoder. Method includes fine-tuning the first autoencoder and the second autoencoder based on the common loss function.