Encoder Training via Numerical Transformation Regression
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Solution Overview
Problem
Existing self-supervised learning approaches in computer vision, such as contrastive learning, are limited in their ability to match the performance of supervised methods for feature learning from sensor data, particularly in tasks like object localization and image segmentation, due to their reliance on binary relationships and lack of incorporation of numerical transformations.
Innovation Solution
A novel regression-based self-supervised learning approach that learns to quantify relationships between sensor data transformations by training an encoder to predict numerical values parameterizing these transformations, thereby extracting features that are more suitable for regression tasks like object localization and pose detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If contrastive learning is used for self-supervised feature learning, then manual annotation burden is reduced, but feature learning performance deteriorates compared to supervised methods
Solution Approach 1:
The patent changes the nature of the learning task from binary classification (contrastive learning) to continuous regression by incorporating numerical transformation parameters. The encoder is trained to predict continuous values representing transformation magnitudes (e.g., rotation angles, scaling factors) rather than just binary relationships, which provides richer supervisory signals and improves feature learning performance while maintaining self-supervised efficiency.
Solution Approach 2:
The patent adds a new dimension to the learning task by incorporating numerical transformation parameters alongside the binary positive/negative pairing. This creates a multi-dimensional learning objective where the model simultaneously learns relationship classification and transformation quantification, enriching the supervisory signal without requiring manual annotations.
2Device complexity
If binary relationship learning is used in self-supervised approaches, then training complexity is reduced, but ability to capture transformation relationships deteriorates
Solution Approach 1:
The patent transforms the learning objective from binary classification to continuous regression by incorporating numerical transformation parameters. The loss function is extended to include regression terms that penalize deviations between predicted and actual transformation values, thereby capturing transformation relationship information while maintaining computational tractability through differentiable operations.
Solution Approach 2:
The patent introduces an intermediary regression head that maps encoder features to numerical transformation parameters. This intermediary component bridges the gap between simple binary relationship learning and complex transformation modeling, allowing the system to capture detailed transformation information while keeping the overall training complexity manageable through modular architecture.
Data Source
AI summary
A computer implemented method of training an encoder to extract features from sensor data comprises generating a plurality of training examples, each training example comprising at least two data representations of a set of sensor data, the at least two data representations related by a transformation parameterized by at least one numerical transformation value; and training the encoder based on a self-supervised regression loss function applied to the training examples. The encoder extracts respective features from the at least two data representations of each training example, and at least one numerical output value is computed from the extracted features. The self-supervised regression loss function encourages the at least one numerical output value to match the at least one numerical transformation value parameterizing the transformation.