Provenance Data for Deep Learning Model Insights
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Solution Overview
Problem
Deep learning models are viewed as 'black boxes,' making it difficult to understand model behavior, accuracy, and producing intelligible results, as existing methodologies for pruning deep neural networks do not provide insights into filter weights and often result in unnecessary resource overhead and potential removal of relevant filters.
Innovation Solution
Collecting and analyzing provenance data during the training phase to generate insights about model strength, reduce model size, and detect reasons for test sample failures, using analytics and reasoning models to optimize the deep learning network by identifying redundant filters and improving resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models are used to process data and provide classifications, then accuracy and output quality are improved, but model interpretability and understanding of model behavior deteriorate due to the black box nature
Solution Approach 1:
The patent introduces provenance data as an intermediary that mediates between the black box deep learning model and the user/analyst. This provenance data captures information about data provenance, model training provenance, and model inference provenance, serving as a bridge that makes model behavior observable and interpretable without altering the model's classification accuracy
Solution Approach 2:
The patent applies preliminary action by collecting and storing provenance data during the training and inference phases before final model deployment. This includes capturing data about training data sources, preprocessing steps, model architecture decisions, and inference parameters in advance, enabling later analysis of model behavior without retraining or re-running the model
2Productivity
If existing pruning methodologies are applied to reduce model size, then model efficiency is improved, but model accuracy and performance deteriorate due to removal of relevant filters
Solution Approach 1:
The patent implements feedback by using collected provenance data to inform the pruning process. The system analyzes provenance information about filter usage, data flow, and model behavior to identify which filters are truly redundant, providing feedback that guides selective pruning while preserving filters that contribute to accurate classifications
Solution Approach 2:
The patent applies parameter changes by modifying the pruning criteria based on provenance analysis. Instead of using fixed thresholds or random pruning, the system dynamically adjusts pruning parameters based on observed model behavior and data provenance, enabling more intelligent removal of filters that minimizes impact on accuracy
3Difficulty of detecting and measuring
If comprehensive provenance data is collected during training, then model insights and interpretability are improved, but computational overhead and training time increase
Solution Approach 1:
The patent applies segmentation by dividing provenance data collection into distinct categories: data provenance (information about training data sources and preprocessing), model training provenance (information about training iterations, hyperparameters, and convergence), and model inference provenance (information about prediction inputs and outputs). This segmented approach enables targeted collection of only necessary provenance information, reducing overall overhead
Data Source
AI summary
A method, computer system, and a computer program product for generating deep learning model insights using provenance data is provided. Embodiments of the present invention may include collecting provenance data. Embodiments of the present invention may include generating model insights based on the collected provenance data. Embodiments of the present invention may include generating a training model based on the generated model insights. Embodiments of the present invention may include reducing the training model size. Embodiments of the present invention may include creating a final trained model.