IBiT: Utilizing Inductive Biases to Create a More Data Efficient Attention Mechanism

• URL: http://arxiv.org/abs/2509.22719v1
• Date: Wed, 24 Sep 2025 17:19:23 GMT
• Title: IBiT: Utilizing Inductive Biases to Create a More Data Efficient Attention Mechanism
• Authors: Adithya Giri,
• Abstract summary: In recent years, Transformer-based architectures have become the dominant method for Computer Vision applications.<n>While Transformers are explainable and scale well with dataset size, they lack the inductive biases of Convolutional Neural Networks.<n>We show that introducing these inductive biases through learned masks allow Vision Transformers to learn on much smaller datasets without Knowledge Distillation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, Transformer-based architectures have become the dominant method for Computer Vision applications. While Transformers are explainable and scale well with dataset size, they lack the inductive biases of Convolutional Neural Networks. While these biases may be learned on large datasets, we show that introducing these inductive biases through learned masks allow Vision Transformers to learn on much smaller datasets without Knowledge Distillation. These Transformers, which we call Inductively Biased Image Transformers (IBiT), are significantly more accurate on small datasets, while retaining the explainability Transformers.

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