To implement continuous learning in a generative model for adaptive behavior in real-time data generation, you can refer to the following steps given below:
- Incremental Learning: Train the model on new data without forgetting previous knowledge by using techniques like rehearsal or Elastic Weight Consolidation (EWC).
- Data Stream Handling: Use a buffer to store a subset of previous data and sample it along with new data to maintain stability.
- Adaptive Loss Function: Use dynamic loss weighting to allow the model to adapt to new data distributions.
- Fine-Tuning: Periodically fine-tune the model on new data without retraining from scratch.
In the above code, we are using the following key points:
- Incremental Learning: Adapts to new data while retaining old knowledge, preventing catastrophic forgetting.
- Data Buffer: Stores a subset of past data to maintain model stability.
- Adaptive Loss: Dynamically adjusts to emphasize learning from new data.
- Fine-tuning: Continuously updates the model without retraining from scratch, enabling real-time adaptation.
Hence, by referring to the above, you can implement continuous learning in a generative model for adaptive behavior in real-time data generation.
