Semantic Coherence Analysis for Deep Neural Network Layer Selection
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Solution Overview
Problem
Deep Neural Networks (DNNs) remain opaque to model developers, making it difficult to reuse trained models for different tasks, as the knowledge learned is hidden and not easily extractable in a human-understandable form, leading to time-consuming and costly development of application-specific DNNs.
Innovation Solution
The Semantic Coherence Analysis (SCA) technique is used to analyze intermediate layers of DNNs, providing a score for individual layers based on semantic coherence, allowing for the selection of layers that produce pure feature clusters, which can be used for transfer learning, incremental learning, and other applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If DNNs are trained for specific tasks using vast amounts of training data, then task performance is improved, but development time and cost increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-training DNNs on large-scale general-purpose datasets (e.g., ImageNet) to learn universal feature representations before deploying them for specific tasks. This preliminary training phase enables the network to acquire transferable knowledge that can be reused across multiple applications, eliminating the need to train from scratch for each new task and significantly reducing development time while maintaining high performance
Solution Approach 2:
The patent implements universality by designing DNNs with intermediate layers that learn task-agnostic feature representations applicable to multiple different tasks. These universal features (such as edges, textures, and shapes in image processing) can be transferred across diverse applications including classification, detection, and segmentation, allowing a single trained model to serve multiple functions and reducing the need for task-specific retraining
2Reliability
If DNNs are trained for one task, then that task performance is improved, but reuse for different tasks becomes difficult due to opacity
Solution Approach 1:
The patent introduces an intermediary analysis layer that acts as a mediator between the trained DNN and potential new applications. This intermediary component performs semantic coherence analysis on intermediate layer activations to identify and extract transferable concepts and features, making the hidden knowledge within the DNN accessible and usable for new tasks without requiring retraining of the entire network
Solution Approach 2:
The patent implements feedback mechanisms by analyzing the activations and feature representations at intermediate layers of the DNN to provide insights about the learned concepts. This feedback loop allows developers to understand what features the network has learned, assess their quality and coherence, and make informed decisions about which features to transfer to new tasks, thereby improving model adaptability and reuse capability
3Loss of information
If intermediate layers of DNNs are analyzed to extract learned concepts, then model interpretability is improved, but analysis complexity increases
Solution Approach 1:
The patent applies the extraction principle by isolating and analyzing only the critical intermediate layers of the DNN that contain transferable feature representations. Instead of analyzing the entire network, the method extracts specific layers with high semantic coherence scores, focusing computational resources on the most valuable portions of the model and reducing overall analysis complexity while maintaining effective knowledge extraction
Solution Approach 2:
The patent utilizes parameter changes by computing semantic coherence scores based on configurable parameters such as the number of clusters, distance metrics, and threshold values. These adjustable parameters allow the analysis complexity to be tuned according to available computational resources and specific application requirements, enabling a balance between thorough knowledge extraction and manageable analysis complexity
Data Source
AI summary
Methods and apparatus are disclosed for interpreting a deep neural network (DNN) using a Semantic Coherence Analysis (SCA)-based interpretation technique. In embodiments, a multi-layered DNN that was trained for one task is analyzed using the SCA technique to select one layer in the DNN that produces salient features for another task. In embodiments, the DNN layers are tested with test samples labeled with a set of concept labels. The output features of a DNN layer are gathered and analyzed according to the concepts. In embodiments, the output is scored with a semantic coherence score, which indicates how well the layer separates the concepts, and one layer is selected from the DNN based on its semantic coherence score. In some embodiments, a support vector machine (SVM) or additional neural network may be added to the selected layer and trained to generate classification results based on the outputs of the selected layer.