Deep Feedback Models
This work addresses the need for more robust and generalizable neural networks in domains like computer vision and medical imaging, though it appears incremental by building on existing feedback concepts.
The paper tackles the problem of static neural networks by introducing Deep Feedback Models (DFMs), which incorporate feedback mechanisms to iteratively refine internal states, resulting in improved performance over feedforward counterparts in object recognition and segmentation tasks, especially under low data or high noise conditions.
Deep Feedback Models (DFMs) are a new class of stateful neural networks that combine bottom up input with high level representations over time. This feedback mechanism introduces dynamics into otherwise static architectures, enabling DFMs to iteratively refine their internal state and mimic aspects of biological decision making. We model this process as a differential equation solved through a recurrent neural network, stabilized via exponential decay to ensure convergence. To evaluate their effectiveness, we measure DFMs under two key conditions: robustness to noise and generalization with limited data. In both object recognition and segmentation tasks, DFMs consistently outperform their feedforward counterparts, particularly in low data or high noise regimes. In addition, DFMs translate to medical imaging settings, while being robust against various types of noise corruption. These findings highlight the importance of feedback in achieving stable, robust, and generalizable learning. Code is available at https://github.com/DCalhas/deep_feedback_models.