arXiv AI By Aleksandr Abramov

Complexity Induction: Compositional Generalization via Structured Label Distortion

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arXiv AI
Aug 28

Complexity Induction: Compositional Generalization via Structured Training Distortion

The paper introduces "complexity induction," a method that distorts training data in a structured way to promote compositional generalization in a standard CNN without changing its architecture. Using synthetic images of colored geometric shapes, the authors encode classes as flat string labels (e.g., "red‑circle") and deliberately omit certain color‑shape combinations from training. Two distortion techniques—mixed labels (soft target distributions based on Jaccard similarity) and expanded dataset (false samples with incorrect labels)—both enable the model to predict unseen class combinations, with mixed labels leveraging the CNN’s embedding structure and expanded training improving embedding factorization. A control experiment with random false labels shows that the effect relies on the structured nature of the distortion rather than noise alone.

By Aleksandr V. Abramov
arXiv Machine Learning
Jul 30

The Advantage of Fine-Grained Training

arXiv:2509. 05130v2 Announce Type: replace Abstract: In classification problems, models are trained to predict a class label based on the input data features.

By Davide Pirovano, Federico Milanesio, Michele Caselle, Piero Fariselli, Matteo Osella
arXiv Machine Learning
Sep 23

eXplaining to Learn (eX2L): Regularization Using Contrastive Visual Explanation Pairs for Distribution Shifts

The paper introduces eXplaining to Learn (eX2L), an interpretable framework that regularizes a classifier by penalizing similarity between Grad‑CAM maps of the main label classifier and a confounder classifier. This approach decorrelates confounding features from latent representations during training. On the Spawrious Many‑to‑Many Hard Challenge benchmark, eX2L outperforms the current state‑of‑the‑art by 5.49% in average accuracy and 10.90% in worst‑group accuracy, while also demonstrating functional domain invariance through explicit label‑nuisance decoupling.

By Paulo Mario P. Medina, Jose Marie Antonio Mi\~noza, Sebastian C. Iba\~nez