Catastrophic forgetting is a phenomenon observed in neural networks and deep learning systems, where learning new information leads to a sudden or severe loss of previously acquired knowledge. This limitation mainly occurs during sequential training on multiple tasks: the network adjusts its parameters for the new task, at the expense of its performance on earlier tasks. Catastrophic forgetting sets artificial intelligence apart from human learning, which naturally accumulates skills without erasing former ones. This phenomenon therefore highlights a fundamental challenge for continual learning and adaptive AI.
Use Cases and Examples
Catastrophic forgetting arises when models are updated on non-stationary data streams, adapted to new domains, or trained on multiple tasks. For example, a voice assistant trained to recognize different languages may forget earlier languages when learning a new one. Recommendation or fraud detection systems, facing evolving behaviors, may also suffer from this phenomenon.
Main Software Tools, Libraries, Frameworks
Several frameworks allow experimentation with strategies to mitigate catastrophic forgetting, including PyTorch and TensorFlow, together with specialized libraries like Avalanche, Continuum, or sequoia. These tools facilitate the implementation of techniques such as Elastic Weight Consolidation (EWC), Learning without Forgetting (LwF), or memory-based regularization.
Latest Developments, Evolutions, and Trends
Recent research is focusing on architectures that enable truly continual learning, inspired by human brain function. There is a growing interest in hybrid methods combining external memory, dynamic regularization, and transfer learning. Advances in foundation models and modular approaches are also opening new perspectives for limiting catastrophic forgetting in real-world and complex environments.