Continual Learning Problem › Summary

Continual learning addresses the challenge of training models on sequences of tasks without forgetting previously learned knowledge. The central obstacle is catastrophic forgetting: when a neural network's parameters are updated to minimize loss on a new task, the updates overwrite the weights that encoded knowledge from previous tasks.

The key concepts from this chapter are:

• Plasticity-stability dilemma: the fundamental trade-off between learning new information quickly (plasticity) and retaining old information accurately (stability). Every continual learning method makes a choice about where to sit on this spectrum.
• Catastrophic forgetting: the sharp, near-total loss of performance on earlier tasks following sequential gradient updates on new tasks. It is caused by the optimizer following the gradient of the new task loss without any awareness of or constraint from old tasks.
• Loss landscape perspective: forgetting occurs because the optimizer moves the parameters from one task's loss basin into another. The two basins are typically separated in high-dimensional parameter space, and unconstrained gradient descent does not find configurations that satisfy both simultaneously.
• Backward Transfer (BWT): the change in performance on earlier tasks caused by training on later tasks. Negative values indicate forgetting; zero means no degradation.
• Forward Transfer (FWT): the change in performance on new tasks attributable to prior task training. Positive values indicate beneficial transfer from shared representations.
• Task-Incremental Learning (Task-IL): the easiest scenario, where task identity is provided at test time. The model can route inputs to task-specific output heads, limiting the forgetting problem to the shared backbone.
• Domain-Incremental Learning (Domain-IL): a medium-difficulty scenario, where the task type is fixed but input distributions shift and no task identity is provided at test time. The shared output head means all domains must be handled by a single classifier.
• Class-Incremental Learning (Class-IL): the hardest scenario, where new classes are added over time and the model must classify across all classes without task identity. Recency bias and growing output space make this particularly challenging.
• Gradient interference: the optimization-level explanation for forgetting, where gradients for new tasks point in directions that conflict with the gradient constraints needed to maintain old task performance.
• Representation drift: the phenomenon where internal representations shift during sequential training, potentially degrading downstream components even when top-level task accuracy is maintained.

The upcoming chapters in this part of the book address the solutions. Regularization-based methods add penalty terms to the loss to protect important parameters from large updates. Replay-based methods maintain a memory of past examples and interleave them with new data. Parameter isolation methods allocate separate model capacity to different tasks to prevent interference. And meta-learning approaches train models specifically to learn new tasks without forgetting, rather than addressing forgetting as an afterthought.

Each approach embodies a different answer to the same question: what resource should we spend to preserve the past?