Neural Network Auxiliary Cost Modulation for Feature Selection

The patent applies local quality by making different parts of the network serve different functions through the attention mechanism. The attention mask selectively highlights relevant regions or features in the input data for the unsupervised task, while other regions are downweighted. This allows the network to focus modeling capacity on locally relevant structures rather than uniformly processing all input data, thereby reducing waste on irrelevant features while maintaining learning effectiveness.

The patent segments the processing of input data by introducing an attention mechanism that divides the input into relevant and irrelevant portions. The attention mask creates a segmented view of the input data, allowing the unsupervised task to process only the relevant segments while ignoring irrelevant portions. This segmentation prevents the network from wasting capacity on irrelevant structures while still learning meaningful patterns from the relevant segments.

The patent addresses the failure of Ladder networks to efficiently learn complex real-world data by introducing local quality through the attention mechanism. Instead of treating all parts of the input equally, the attention mask assigns different weights to different regions, allowing the network to focus on locally important features while maintaining efficient iterative training. This selective focus enables the network to handle complex scenes with multiple objects by concentrating computational resources on relevant local structures.

The patent introduces an attention mask as an intermediary component between the input data and the Ladder network processing. This intermediary selectively modulates the input signal, enhancing relevant features and suppressing irrelevant ones before they enter the main network processing. This intermediary layer enables the network to efficiently learn complex data representations by pre-processing the input to highlight important structures, thereby bridging the gap between simple iterative training and complex real-world applications.

The patent resolves the contradiction between capturing complete input structure and learning specific features efficiently by applying local quality through the attention mechanism. The attention mask dynamically assigns different weights to different parts of the input data during training, allowing the network to maintain awareness of the complete structure while concentrating learning capacity on locally important regions. This enables faster learning of specific features like different objects in an image without losing the overall structural context.

The patent introduces dynamics by making the attention mask adaptive rather than static. The attention weights are dynamically adjusted during training based on the learned relevance of different input regions. This dynamic mechanism allows the network to automatically direct different parts of the network to concentrate on different parts of the data as needed, while maintaining the ability to capture complete input structure when necessary. The dynamic nature enables flexible adaptation to different learning stages and data characteristics.