Contrastive Neural Network Training for Novel Pattern Adaptation
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Solution Overview
Problem
Existing machine learning models in neural networks struggle to adapt to new data sets with novel patterns not present in the training data, leading to poor performance in real-world settings where historical and new data distributions may differ.
Innovation Solution
A system and method for training a contrastive neural network in an active learning environment, utilizing a pre-training manager to pre-train a neural network with historical data, a training manager to update weights and learn novel features in new data, and a prediction manager to leverage prior knowledge and predict labels for new data, effectively combining borrowed and updated weights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a neural network is trained with historical data using supervised learning, then the model achieves good performance on training data, but it fails to adapt to new data sets with novel patterns not present in the training data
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network with historical data to establish a baseline model, then later updating it with new data. The pre-training manager feeds historical data to establish initial weights, and the training manager later updates these weights with new data patterns, allowing the model to adapt sequentially rather than requiring complete retraining.
Solution Approach 2:
The patent implements dynamics by making the neural network weights updateable and adaptable over time. Instead of a static trained model, the system allows weights to be borrowed from pre-trained networks and then updated with new data, creating a dynamic model that evolves to accommodate novel patterns while retaining knowledge from historical data.
2Ease of manufacture
If the model relies on training and test data being drawn from the same distribution, then training is simpler, but performance deteriorates in real-world settings where data distributions differ
Solution Approach 1:
The patent applies parameter changes by modifying the weight parameters of the neural network based on new data distributions. The training manager updates the weights borrowed from the pre-trained network to adapt to new data characteristics, allowing the model to adjust its parameters when faced with different data distributions in real-world applications.
Solution Approach 2:
The system implements feedback by using the new dataset to evaluate and update the pre-trained model. The training process incorporates feedback from the new data to adjust weights, creating a loop where the model continuously improves its performance based on real-world data distributions rather than assuming static training conditions.
3Stability of the object's composition
If supervised learning dictates an output for a given determinate input, then the model provides consistent predictions, but it cannot learn or adapt to novel patterns in new data
Solution Approach 1:
The patent uses preliminary action by establishing a pre-trained model with consistent prediction capabilities through supervised learning on historical data, then later applying updates with new data to introduce adaptability. The pre-training phase ensures consistent baseline predictions, while subsequent training phases enable learning of novel patterns.
Solution Approach 2:
The system achieves both consistency and adaptability through dynamic weight updates. The neural network maintains its structured architecture and learning framework (providing consistency) while allowing weight parameters to be updated with new data (enabling adaptability). This dynamic approach allows the model to evolve without losing its fundamental predictive capabilities.
Data Source
AI summary
Embodiments relate to a system, program product, and method for training a contrastive neural network (CNN) in an active learning environment. A neural network is pre-trained with labeled data of a historical (first) dataset. The CNN is trained for a new (second) dataset by applying the new dataset and contrasting the new dataset against the historical dataset to extract novel patterns. Weights of a knowledge operator from the pre-trained neural network are borrowed. Features novel to the new dataset are learned, including updating weights of the knowledge operator. The borrowed knowledge operator weights are combined with the updated knowledge operator weights. The CNN is leveraged to predict one or more labels for the new dataset as output data.