Xiaojie Xu

Scalar Invariant Networks with Zero Bias

Haolin Ye

Just like weights, bias terms are the learnable parameters of many popular machine learning models, including neural networks. Biases are th… (see more)ought to enhance the representational power of neural networks, enabling them to solve a variety of tasks in computer vision. However, we argue that biases can be disregarded for some image-related tasks such as image classification, by considering the intrinsic distribution of images in the input space and desired model properties from first principles. Our findings suggest that zero-bias neural networks can perform comparably to biased networks for practical image classification tasks. We demonstrate that zero-bias neural networks possess a valuable property called scalar (multiplication) invariance. This means that the prediction of the network remains unchanged when the contrast of the input image is altered. We extend scalar invariance to more general cases, enabling formal verification of certain convex regions of the input space. Additionally, we prove that zero-bias neural networks are fair in predicting the zero image. Unlike state-of-the-art models that may exhibit bias toward certain labels, zero-bias networks have uniform belief in all labels. We believe dropping bias terms can be considered as a geometric prior in designing neural network architecture for image classification, which shares the spirit of adapting convolutions as the transnational invariance prior. The robustness and fairness advantages of zero-bias neural networks may also indicate a promising path towards trustworthy and ethical AI.

2023-01-01

NeurReps (published)

doi.org

openreview.net

Towards Reliable Neural Specifications

Chuqin Geng

Nham Le

Xiaojie Xu

Zhaoyue Wang

Arie Gurfinkel

Xujie Si

2023-01-01