Neural Network Data Segmentation via Tagged Masking
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Solution Overview
Problem
Existing deep neural networks face challenges in efficiently learning to represent complex data, such as images or videos, due to a lack of mechanisms to direct different parts of the network to focus on specific data elements, particularly in unsupervised learning for tasks like image segmentation.
Innovation Solution
A method involving tagging data elements within the neural network, where tags are generated, applied as masks to input data, and updated based on distance calculations to guide the network in separating objects, allowing for improved segmentation and unsupervised learning by iteratively refining these tags and reconstructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional autoencoder or ladder network structures are used for unsupervised learning, then the network can process complex data, but the learning speed is slow and the ability to learn abstract features is limited
Solution Approach 1:
The patent applies segmentation by dividing the input data into multiple segments using generated tags that mask different portions of the data. Each segment is processed separately through the neural network, allowing parallel processing and faster learning while maintaining the ability to capture complex features through the combination of segmented representations
Solution Approach 2:
The patent implements local quality by assigning different tags to different segments of the input data, allowing each segment to be processed with specialized attention. This enables different parts of the network to develop specialized feature detectors for different data regions, improving both learning speed through focused processing and representation accuracy through localized feature extraction
2Adaptability or versatility
If ladder network structures are used, then iterative training can be performed, but the network fails to efficiently concentrate on representing different parts of the data
Solution Approach 1:
The patent applies preliminary action by generating tags and creating segmented versions of the input data before feeding them to the neural network. This pre-processing step organizes the data into meaningful segments that guide the network's attention, enabling it to efficiently concentrate on different parts of the data from the beginning of training rather than requiring extensive iterative refinement
Solution Approach 2:
The patent implements feedback by using the reconstructed segments and their corresponding tags to update the segmentation process. The network's output is fed back to refine the tags and segments, creating an iterative improvement loop that enhances both the adaptability of the representation and the precision of the segmentation over time
3Device complexity
If unsupervised learning is applied to segmentation tasks, then labeled data requirements are reduced, but the learning process becomes slower and less effective for complex scenes
Solution Approach 1:
The patent applies self-service by enabling the neural network to automatically generate its own training segments and tags from unlabeled input data without requiring external annotation. The system serves itself by creating the segmented structure needed for effective learning from the raw input, eliminating the need for manually labeled data while maintaining segmentation efficiency through self-organized feature learning
Data Source
AI summary
Disclosed is a computer-implemented method for segmenting input data. In the method a plurality of tags is generated; the input data is masked with the plurality of tags; a plurality of output reconstructions is generated by inputting the plurality of masked input data to one of the following: a denoising neural network, a variational autoencoder; a plurality of values representing distances of each plurality of output reconstructions to the input data are determined; a plurality of updated versions of input data is generated by applying at least one of the determined values representing distances of each plurality of output reconstructions to the input data; and updated output reconstructions are generated by inputting the plurality of updated versions of input data to one of the networks. Also disclosed is a method for training the network and a processing unit.