Initial Guessing Bias: How Untrained Networks Favor Some Classes

• URL: http://arxiv.org/abs/2306.00809v6
• Date: Fri, 08 Nov 2024 14:27:36 GMT
• Title: Initial Guessing Bias: How Untrained Networks Favor Some Classes
• Authors: Emanuele Francazi, Aurelien Lucchi, Marco Baity-Jesi,
• Abstract summary: We show that the structure of a deep neural network (DNN) can condition the model to assign all predictions to the same class, even before the beginning of training. We prove that, besides dataset properties, the presence of this phenomenon is influenced by model choices including dataset preprocessing methods. We highlight theoretical consequences, such as the breakdown of node-permutation symmetry and the violation of self-averaging.
• Score: 0.09103230894909536
• License:
• Abstract: Understanding and controlling biasing effects in neural networks is crucial for ensuring accurate and fair model performance. In the context of classification problems, we provide a theoretical analysis demonstrating that the structure of a deep neural network (DNN) can condition the model to assign all predictions to the same class, even before the beginning of training, and in the absence of explicit biases. We prove that, besides dataset properties, the presence of this phenomenon, which we call \textit{Initial Guessing Bias} (IGB), is influenced by model choices including dataset preprocessing methods, and architectural decisions, such as activation functions, max-pooling layers, and network depth. Our analysis of IGB provides information for architecture selection and model initialization. We also highlight theoretical consequences, such as the breakdown of node-permutation symmetry, the violation of self-averaging and the non-trivial effects that depth has on the phenomenon.

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