Task-Agnostic Exemplar-Free Domain-Incremental Learning

Autonomous Domain-Incremental Learning (DIL) requires models to sequentially acquire new visual domains without explicit task identifiers, exemplar storage or catastrophic forgetting. Current methodologies face a fundamental trilemma: replay-based methods compromise data privacy, regularization restricts model plasticity and retrieval-based routing degrades as latent representations drift over time. We introduce Task-Agnostic Continual Training via Independent Components (TACTIC), an exemplar-free framework that resolves this trilemma through a unified geometric principle: constraining all learned representations to a shared unit hypersphere ${\mathbb{S}}^{d-1}$. TACTIC operationalizes this principle via three synergistic mechanisms. First, a sub-center geometric head allocates multiple angular anchors per class, providing the intra-class flexibility necessary to mathematically prevent the semantic collapse endemic to shared classifiers. Second, a feature disentanglement objective explicitly separates domain-specific style from universal semantics. By enforcing statistical independence (HSIC) between these representations, TACTIC guarantees stable, drift-free routing at inference without relying on task metadata. Third, a hyperspherical analytic memory synthesizes per-class Gaussian statistics and projects them back onto ${\mathbb{S}}^{d-1}$, maintaining manifold compatibility while supplying boundary-anchoring hard negatives. Theoretically, we establish intra-class interference bounds that guarantee the geometric safety of our analytic replay. Empirically, TACTIC establishes a new state-of-the-art exemplar-free DIL performance across diverse benchmarks (DomainNet, Office-Home, CORe50 and Camelyon17). Crucially, by isolating parameters and eliminating cross-domain gradient interference, TACTIC outperforms existing methodologies, all while maintaining a minimal storage footprint of less than 1.5 MB.