Autoencoder Bottleneck Mapping for ML Embedding Analysis
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Solution Overview
Problem
Existing machine learning models in Advanced Driver Assistance Systems (ADAS) are treated as black boxes, making it difficult to understand their inner workings, especially when dealing with out-of-sample data, which limits the ability to improve model performance and optimize data-sets.
Innovation Solution
Training an autoencoder to perform dimensionality reduction on output data from embedding layers, using a custom loss function that combines reconstruction loss and error between the autoencoder bottleneck and dimensionality reduced data, allowing visualization and analysis of out-of-sample data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional dimensionality reduction techniques (T-SNE, UMAP, Large Vis) are used to visualize embedding layer data, then in-sample data can be effectively visualized, but the method cannot generalize to out-of-sample data
Solution Approach 1:
The patent creates a simplified copy of the embedding layer by training an autoencoder to reproduce the dimensionality-reduced output. The autoencoder learns to map high-dimensional embedding data to low-dimensional visualized data, creating a reusable model that can process any input data through the same embedding layer without requiring re-execution of the original complex dimensionality reduction algorithm.
Solution Approach 2:
The autoencoder is trained in advance on a training data set to learn the mapping from embedding layer outputs to dimensionality-reduced visualizations. This preliminary training creates a pre-configured model that can immediately process out-of-sample data without requiring the computationally expensive T-SNE or UMAP algorithms to be re-executed for each new data set.
2Productivity
If ML models are treated as black boxes with unknown learned principles, then model development is simpler, but it becomes difficult to improve model performance and optimize data-sets
Solution Approach 1:
The patent introduces an intermediary visualization layer between the black-box ML model and the developer. By training an autoencoder to reproduce the embedding layer outputs and visualizing them, developers gain insight into what the model has learned without needing to open the black box. The visualizations reveal patterns, clusters, and relationships in the learned representations, enabling informed decisions about model improvement and data-set optimization.
3Measurement precision
If data from hidden layers is extracted for analysis, then understanding of model internals is improved, but the high dimensionality (hundreds or thousands of dimensions) makes conceptualization impossible
Solution Approach 1:
The patent applies dimensionality reduction to transform high-dimensional embedding layer data (hundreds or thousands of dimensions) into low-dimensional visualizations (typically 2D or 3D). The autoencoder learns to compress the high-dimensional data while preserving the essential structure and relationships, enabling human conceptualization through visual inspection of the reduced-dimensional representations.
Data Source
AI summary
A method of training an autoencoder for analyzing a machine learning model, the method includes extracting output data from an embedding layer of the machine learning model operating on in-sample data. The method includes performing dimensionality reduction on at least a portion of the extracted output data from the embedding layer to obtain first dimensionality-reduced data. The method includes training the autoencoder to generate corresponding intermediate dimensionality-reduced data at a bottleneck of the autoencoder from at least the portion of the extracted output data.