Complexity Induction: Compositional Generalization via Structured Training Distortion
Quick summary
arXiv:2608.21464v2 Announce Type: replace-cross Abstract: We demonstrate that structured distortion of training data - which we term complexity induction - can induce compositional generalization in a standard CNN classifier without architectural modification. Using synthetic images of colored geometric shapes, we encode classes as flat string labels (e.g., "red-circle") with no explicit attribute decomposition, and exclude certain color-shape combinations from training entirely. We apply two distortion methods derived from Jaccard string similarity between class names: mixed labels (soft targ
Key takeaways
- arXiv:2608.21464v2 Announce Type: replace-cross Abstract: We demonstrate that structured distortion of training data - which we term complexity induction - can induce compositional generalization in a standard CNN classifier without architectural modification.
- Using synthetic images of colored geometric shapes, we encode classes as flat string labels (e.g., "red-circle") with no explicit attribute decomposition, and exclude certain color-shape combinations from training entirely.
- We apply two distortion methods derived from Jaccard string similarity between class names: mixed labels (soft targ
Why it matters
The importance of “Complexity Induction: Compositional Generalization via Structured Training Distortion” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.
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