Temporal adaptation aids object recognition in deep convolutional neural networks in suboptimal viewing scenario's
We compare how intrinsic and recurrent temporal adaptation mechanisms in deep neural networks affect object recognition under challenging conditions. We find intrinsic adaptation is superior for recognizing simple, high-contrast objects in noise, whereas recurrent adaptation better maintains coherence under dynamic occlusion and improves novelty detection. These results indicate that robust object recognition likely depends on multiple parallel adaptation strategies.
Apr 3, 2025
