Continual Learning Catastrophic Forgetting is training neural networks sequentially on tasks without forgetting previously learned tasks, addressing the catastrophic forgetting problem where new learning overwrites old knowledge — enabling lifelong AI systems. Continual learning mimics human learning. Catastrophic Forgetting neural networks trained on sequence of tasks forget earlier tasks. Weights optimized for task 2 become poor for task 1. Plasticity-stability dilemma: adapt to new tasks (plasticity) while maintaining old knowledge (stability). Task Incremental Learning tasks arrive sequentially. Network must: learn current task, remember previous tasks. Task identity available at test time (task-specific output head). Class Incremental Learning new classes arrive over time. No task boundaries. Single output head. More difficult than task incremental. Domain Incremental Learning same task, data distribution changes. Covariate shift between tasks. Replay and Experience Replay remember subset of old data, replay during new task training. Interleave old and new task. Effective but requires storing past data. Generative Replay generate pseudo-examples of old tasks via generative model. No storage of real data but generative model adds complexity. Elastic Weight Consolidation (EWC) track importance of weights for previous tasks via Fisher information matrix. Penalize changes to important weights: loss = new_loss + λ Σ F_i (w_i - w_i*)^2. F_i = Fisher information (importance). Synaptic Importance different parameterizations of importance. Elastic weight consolidation, synaptic importance, MAS (Memory Aware Synapses). Memory Consolidation biological inspiration: brain consolidates memories during sleep. Offline consolidation phase after task. Dynamic Expansion add new neurons for new tasks. Gradually increase capacity. Avoid catastrophic forgetting through architecture expansion. PackNet mask learning: learn binary masks per task indicating which weights to use. Enables selective reuse. Adapter Modules small trainable modules for each task. Keep base network frozen. Task-specific learning through adapters. Prompt Learning condition network on task-specific prompts. Learn prompts, reuse backbone. Similar to adapter idea. Domain-Aware Learning use domain information to guide consolidation. Separate out domain-specific and task-specific factors. Sparse Representations sparse activations naturally avoid interference. Active neurons for task 1 different from task 2. Disentangled Representations learn separated representations for different factors. Disentanglement reduces interference. Backward Transfer learning new task improves old task performance. Positive: generalization. Forward Transfer learning old task helps new task. Domain overlap and transfer. Meta-Learning for Continual Learning learn learning algorithm that avoids forgetting. MAML, other meta-learning approaches. Rehearsal-Free Methods don't replay old data. Replay impractical at scale. Pseudo-Rehearsal synthetic examples of old tasks. Generate via generative model. Curriculum Learning order tasks for efficient learning. Easier tasks first. Smooth transition between tasks. Evaluation Metrics final accuracy on all tasks, backward transfer, forward transfer. Metrics differ from standard supervised learning. Benchmarks Permuted MNIST, Split CIFAR-10/100, ImageNet-100. Biological Plausibility brain continually learns. Synaptic consolidation, neuromodulation mechanisms. Practical Challenges computational efficiency (repeated learning slows down). Scalability (many tasks). Applications robots learning sequentially, dialogue systems improving over time, personalized ML systems. Continual learning enables AI systems learning throughout deployment rather than static pretrained models.