Generative Adversarial Network With Dynamic Capacity Expansion
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Solution Overview
Problem
Conventional machine learning models face challenges in learning multiple tasks continuously without forgetting previously acquired knowledge, due to lack of plasticity and scalability in their neural networks, leading to catastrophic forgetting.
Innovation Solution
Implementing a generative adversarial network with a generator network and a discriminator network that generates synthetic training samples to emulate actual samples, allowing the model to reinitialize and retrain while preserving knowledge of previous tasks, and expanding network capacity as needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the discriminator network is reinitialized for continual learning, then adaptability to new tasks is improved, but knowledge of previous tasks is lost
Solution Approach 1:
The generator network creates synthetic training samples that copy the statistical properties and features of previously learned tasks. These synthetic samples serve as a memory buffer, allowing the discriminator to be reinitialized for new tasks while the generator preserves knowledge through synthesized data that emulates previous task distributions.
Solution Approach 2:
The system separates the knowledge preservation function (generator network) from the task adaptation function (discriminator network). The generator maintains synthetic samples from previous tasks independently, while the discriminator focuses on learning new tasks, allowing both goals to be achieved without interference.
2Measurement precision
If the generator network capacity is expanded to preserve more knowledge, then classification accuracy is improved, but device complexity increases
Solution Approach 1:
The generator network capacity is made dynamic and adaptive. Rather than expanding capacity uniformly, the system selectively expands only when necessary based on task requirements. The architecture allows for flexible adjustment of network size, balancing accuracy needs against computational complexity constraints.
Data Source
AI summary
A method may include training a machine learning model to perform a first task before training the machine learning model to perform the second task. The machine learning model includes a generator network and a discriminator network. The training includes training, based on a first training sample associated with the first task, the discriminator network to perform the first task. The generator network may be trained to generate a first synthetic training sample emulating the first training sample. The discriminator network trained to perform the first task may be reinitialized in order for the discriminator network to be trained, based on a second training sample, to perform the second task. The reinitialized discriminator network may be further retrained, based on the first synthetic training sample, to perform the first task. Related systems and articles of manufacture, including computer program products, are also provided.