Class-incremental learning (CIL) is a continual learning scenario where new output classes are added over time, and the model must learn to distinguish among all classes seen so far — including both old and new ones — without access to data from previous tasks.

The Challenge

• Task 1: Learn to classify classes {cat, dog}.
• Task 2: Now add classes {bird, fish}. The model must classify among {cat, dog, bird, fish} — but only has training data for bird and fish.
• Task 3: Add {horse, cow}. The model must handle all 6 classes with only horse and cow data available.

Why CIL is Hard

• Output Space Grows: The classification head must expand to accommodate new classes, and the model must maintain decision boundaries between all classes.
• No Task ID at Test Time: Unlike task-incremental learning, the model doesn't know which task a test example belongs to — it must distinguish among all classes simultaneously.
• Class Imbalance: During training on a new task, only new classes have available data, creating severe imbalance that biases the model toward recent classes.
• Decision Boundary Shift: As new classes are added, old decision boundaries need adjustment even though old data isn't available.

Key Methods

• iCaRL: Stores exemplars from old classes and uses nearest-class-mean classification in feature space rather than the output layer.
• LUCIR: Uses cosine normalization and less-forget constraint to maintain balanced representations.
• PODNet: Preserves intermediate representations through pooled outputs distillation across spatial dimensions.
• DER (Dark Experience Replay): Stores old examples with their logits and uses knowledge distillation during replay.
• Bias Correction: Explicitly correct the bias toward new classes in the classification layer.

Evaluation Protocol

• Report accuracy on all seen classes after each incremental step.
• The key metric is the average incremental accuracy — the average of accuracies across all steps.
• Compare against the joint training upper bound (training on all data simultaneously).

Class-incremental learning is considered the hardest standard continual learning setting and is the most representative of real-world deployment scenarios where new categories continuously emerge.