Dynamic Input Activation Network is an experimental machine learning project focused on improving neural network efficiency by dynamically activating only the most relevant inputs during training and inference. The goal is to explore how selective input activation can reduce computational cost while maintaining or enhancing model accuracy.
The system is designed around a modular PyTorch implementation, enabling easy testing of various activation strategies and sparsity levels. By simulating selective attention mechanisms, the model investigates potential benefits for both small-scale experiments and scalable deep learning systems.
Highlights
- Selective activation: Activates neurons dynamically based on input features and relevance.
- Reduced computation: Saves energy and accelerates inference by avoiding unnecessary activations.
- Adaptable framework: Easily extendable to different network architectures and datasets.
- Research-oriented: Built to test hypotheses around sparsity, adaptive learning, and attention-based optimization.
This project reflects an ongoing exploration of smarter, more efficient machine learning approaches, inspired by biological systems and the quest for lightweight AI models.