Neural Network Incremental Training via Ground Truth Preservation
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Solution Overview
Problem
Neural networks face challenges when classifying new data points that were not part of the initial training dataset, leading to misclassification, as they are not adequately represented in the training data, requiring incremental training to update the model without significantly altering the original classification accuracy.
Innovation Solution
The method involves training a neural network using a combination of original and incremental training data, where the original data classification probabilities are used as ground truth for the original data and manual classification is used for incremental data, with a custom loss function to update the model, ensuring consistency with the original model's performance on test data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the neural network is retrained with combined original and incremental training data, then the model can classify new data points accurately, but the training time and computational resources increase significantly
Solution Approach 1:
The system pre-processes incremental training data by generating synthetic samples and augmenting existing data before formal retraining. This preliminary preparation reduces the actual retraining time while ensuring the model learns from comprehensive data coverage, thus resolving the contradiction between accuracy and training time.
Solution Approach 2:
The system dynamically adjusts training parameters such as learning rate, batch size, and number of epochs based on the characteristics of incremental data. By optimizing these parameters, the system achieves accurate classification with reduced training time and computational resources.
2Adaptability or versatility
If the neural network is retrained with incremental training data, then new data points are classified accurately, but the original classification accuracy may deteriorate
Solution Approach 1:
The system merges original training data with incremental training data into a unified training set, ensuring both datasets are properly weighted and integrated. This combination allows the model to learn from both historical and new data, maintaining original accuracy while gaining adaptability to new data patterns.
Solution Approach 2:
The system implements feedback mechanisms where the model's performance on both original and new data is continuously monitored during retraining. Based on this feedback, the training process is adjusted to prevent catastrophic forgetting and maintain original classification accuracy while learning new patterns.
3Reliability
If the complete original training dataset is used for incremental training, then classification accuracy is maintained, but the storage requirements and data processing complexity increase
Solution Approach 1:
The system segments the training process into multiple phases: data selection, synthetic sample generation, model training, and evaluation. This segmentation reduces processing complexity at each stage while maintaining overall accuracy by focusing computational resources on critical tasks.
Solution Approach 2:
The system extracts only the most relevant and representative samples from the original training data that are needed for incremental training. By selecting a subset of critical data points rather than processing the complete dataset, the system reduces storage requirements and processing complexity while preserving classification accuracy.
Data Source
AI summary
Systems and methods are disclosed for training a previously trained neural network with incremental dataset. Original train data is provided to a neural network and the neural network is trained based on the plurality of classes in the sets of training data and/or testing data. The connected representation and the weights of the neural network is the model of the neural network. The trained model is to be updated for an incremental train data. The embodiments provide a process by which the trained model is updated for the incremental train data. This process creates a ground truth for the original training data and trains on the combined set of original train data and the incremental train data. The incremental training is tested on a test data to conclude the training and to generate the incremental trained model, minimizing the knowledge learned with the original data. Thus, the results remain consistent with the original model trained by the original dataset except the incremental train data.