Research1d ago
Meta-Learning Compression For TinyML Object Detection
Under 100KB of memory limits how microcontrollers handle object detection. Researchers introduced Adaptive Hierarchical Compression, using a MAML-based framework to adapt compression ratios via gradient descent in five steps. This approach targets FPN redundancy patterns to reduce catastrophic forgetting. It enables more flexible feature storage for practitioners deploying vision models on constrained hardware.