Propose an implementation strategy for efficient local contrast normalization in your object detection model, given the need to balance accuracy and computational resources.
Use separable convolution to compute feature maps of local means and standard deviations. Then apply element-wise subtraction and division to normalize the image. This approach reduces computation while maintaining robustness to varying lighting conditions.
Propose an architecture that leverages forward mode accumulation to achieve real-time computation of gradients with multiple outputs. Explain the tradeoffs between using forward mode and backward mode in this scenario.
Implement forward mode accumulation to compute gradients for prediction and additional metrics simultaneously, reducing response time. Tradeoff: increased memory usage compared to backward mode, but allows for real-time computation of gradients with multiple outputs.