Image Classifier Using Factorized Representation for Shortcut Learning
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Solution Overview
Problem
Image classifiers for automated driving often rely on 'shortcut learning' where they associate object types with easily determinable attributes like color rather than shape, leading to misclassification in varied situations, and require extensive training data to overcome these biases.
Innovation Solution
An image classifier with an encoder network that factorizes representations into independent components for shape, color, lighting, and texture, using an association unit to selectively provide relevant information to classification head networks, and a training method involving factor classification head networks and 'cheap' factor training images to reduce reliance on unwanted associations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the image classifier is trained with extensive labelled training data to overcome shortcut learning biases, then the classification reliability improves, but the training time and computational resources increase
Solution Approach 1:
The encoder network is pre-trained to produce factorized representations that separate shape, color, and texture information before the main classification task begins. This preliminary structuring of the representation space allows the classifier to learn correct associations more efficiently during training, reducing the need for extensive labelled data while maintaining reliability
Solution Approach 2:
The representation Z is segmented into multiple independent components z1, ..., zK that correspond to different visual factors (shape, color, texture). This segmentation allows the classification head networks to receive targeted information through the association unit, preventing shortcut learning and improving training efficiency with fewer labelled examples
2Measurement precision
If the image classifier uses all representation components for both object and attribute classification, then the classification accuracy improves, but the tendency to learn unwanted associations increases
Solution Approach 1:
The association unit extracts and selectively provides only the relevant representation components for each classification task. For object classification, it provides components related to shape and structure while excluding color components that would lead to unwanted associations. This extraction mechanism maintains classification accuracy while preventing shortcut learning
Solution Approach 2:
Different subsets of representation components are provided to different classification head networks based on their specific needs. The object classification head receives shape-related components, while the attribute classification head receives color and texture components. This local quality assignment ensures each network receives appropriate information without learning unwanted associations
Data Source
AI summary
An image classifier for classifying an input image x with respect to combinations of an object value o and an attribute value. The image classifier includes an encoder network that is configured to map the input image to a representation comprising multiple independent components; an object classification head network configured to map representation components of the input image to one or more object values; an attribute classification head network configured to map representation components of the input image to one or more attribute values; and an association unit configured to provide, to each classification head network, a linear combination of those representation components of the input image x that are relevant for the classification task of the respective classification head network. A method for training the image classifier is also provided.