Notes

Data Replay Methods

Data Replay methods in continual learning involve storing previously seen data and reusing it to help the model retain information from previous tasks when learning new tasks. This can be done in several ways, however the most common are:

• Experience Replay: this method involves storing a dataset of previous experiences, such as input-output pairs, and randomly sampling from this dataset during training on new tasks to provide additional examples for the model to learn from.
• Generative Replay: this method involves training a generative model, such as a Generative Adversarial Network (GAN) or Variational Autoencoder (VAE), on the previous task data, and then using the generative model to generate new examples from the previous task to be used during training on new tasks.

Deep Generative Replay Data Generative Replay is a method in continual learning that uses a generative model to generate new examples from previous tasks to be used during training on new tasks. The general process of Data Generative Replay is as follows:

1. A generative model, such as a Generative Adversarial Network (GAN) or Variational Autoencoder (VAE), is trained on data from a previous task.
2. The generative model is then used to generate new examples from the previous task.
3. These generated examples are added to the training set for the new task, along with the examples from the new task.
4. The model is then trained on the combined dataset (new task examples + generated examples from previous task)
5. Steps 1-4 are repeated for each new task.