Deep Feature Extraction via Tessellation Masking
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Solution Overview
Problem
Deep learning models, such as deep neural networks, operate as 'black box' systems, making it difficult to understand how decisions are made and complicating the modification or refinement of deep features, which are crucial for effective computer vision applications.
Innovation Solution
The implementation of deep feature extraction and training tools that tessellate imaging data into cells, mask subsets, and process masked outputs to identify and visualize deep features, allowing for the understanding and modification of features that lead to correct or incorrect decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If deep learning models are used for computer vision applications, then the effectiveness and automatic feature extraction capability are improved, but the interpretability and modifiability of the models deteriorate due to black box operation
Solution Approach 1:
The patent introduces an intermediary system comprising visualization tools, feature extraction modules, and analysis interfaces that mediate between the black box deep learning model and the user. This intermediary layer captures internal feature representations, processes them through various analysis methods, and presents interpretable visualizations without modifying the core model architecture, thereby maintaining automation while improving interpretability.
2Loss of information
If deep features are extracted and analyzed, then the understanding and refinement capability of the model is improved, but the computational load and processing time increase
Solution Approach 1:
The patent extracts only the essential and most informative features from the deep learning model's internal representations using selective feature extraction techniques. By identifying and isolating key features that contribute most to model decisions, the system reduces the volume of data requiring analysis while maintaining comprehensive understanding of model behavior, thereby reducing computational overhead.
Solution Approach 2:
The patent implements partial analysis by focusing computational resources on analyzing only certain layers, features, or subsets of data that provide the most valuable insights. Rather than comprehensively analyzing every aspect of the deep learning model, the system selectively applies analysis to critical components, achieving sufficient understanding with reduced computational expenditure.
3Loss of information
If the entire imaging data is processed, then the completeness of analysis is improved, but the computational complexity and resource requirements worsen
Solution Approach 1:
The patent segments the imaging data and model processing into discrete, manageable components including dividing images into regions of interest, partitioning feature extraction by layer or type, and organizing analysis tasks into modular stages. This segmentation enables parallel processing, reduces memory requirements, and allows selective processing of only relevant data portions while maintaining comprehensive coverage.
Data Source
AI summary
Deep feature extraction and training tools and processes may facilitate extraction and understanding of deep features utilized by deep learning models. For example, imaging data may be tessellated and masked to generate a plurality of masked images. The masked images may be processed by a deep learning model to generate a plurality of masked outputs. The masked outputs may be aggregated for each cell of the tessellated image and compared to an original output for the imaging data from the deep learning model. Individual cells and associated image regions having masked outputs that correspond to the original output may comprise deep features utilized by the deep learning model.