InTAct: Functional Preservation in Neural Networks for Continual Learning

InTAct: Functional Preservation in Neural Networks for Continual Learning

Continual learning in artificial intelligence faces a crucial challenge when models must adapt to new domains without losing previously acquired competencies. Current approaches, including techniques based on parameterized prompts, experience representational drift that alters essential internal features for previous tasks. InTAct emerges as an innovative solution that preserves the functional behavior of shared layers without requiring frozen parameters or storage of historical data, ensuring coherence in the characteristic activation ranges of each task while enabling adaptations in non-critical regions 🧠.

Knowledge Protection Mechanism

The InTAct methodology identifies specific activation intervals linked to each learned task and restricts model updates to preserve consistency within those critical ranges. Instead of directly immobilizing parametric values, the system regulates the functional role of important neurons, containing representational drift where prior knowledge resides. This strategy is architecture-independent and integrates seamlessly into prompt-based frameworks, providing an additional layer of protection without compromising the overall learning process.

• Automatic identification of task-specific activation ranges
• Regulation of updates without parametric freezing
• Compatibility with diverse neural architectures

InTAct stabilizes critical functional regions that encode past tasks while allowing the model to learn new transformations in unprotected zones

Experimental Evaluation and Applications

Tests conducted on domain shift benchmarks such as DomainNet and ImageNet-R demonstrate that InTAct consistently reduces drift in representations and significantly improves performance. Experiments record increases of up to 8 percentage points in Average Accuracy compared to reference methods, establishing a new paradigm in the balance between stability and plasticity. The technique consolidates essential functional areas that encode previous tasks while allowing the model to absorb new transformations in unprotected regions, offering a robust solution for real-world scenarios where input domains evolve constantly.

• Sustained improvement in average accuracy across domains
• Significant reduction in representational drift
• Maintained adaptability in dynamic environments

Implications for the Future of Machine Learning

It seems that neural networks can finally remember where they left the keys to prior knowledge while rummaging through the drawer of shifting domains. This capability for selective preservation marks a milestone in the development of more efficient and versatile AI systems, capable of evolving without losing their previous operational essence 🔑.