Implicit Bias Analysis in The Training of Compact Neural Networks For Inverse Problems

This paper suggests three new techniques to improve tiny neural networks’ inverse problem-solving. Traditional approaches, like basic linear regression models, lack the flexibility and bias reduction needed to handle complex real-world challenges. Our recommended methods—BRT, ABN, and LFD—can solve these issues. The BRT technique balances bias reduction with data fitting by using L1 and L2 regularization to handle hidden biases. Change batch normalization parameters on the fly with ABN for additional possibilities. This optimizes feature scaling and accuracy while reducing false positives. But LFD increases F1 scores by applying several loss functions. This is important for avoiding false positives and negatives. Some of our experiments used phony data, yet they were positive. The comparative analysis reveals that recommended solutions outperform regular techniques on key performance parameters. ABN is precise, LFD is accurate, and BRT has an average F1 number. These results demonstrate the value of new ideas tailored to each assignment. However, our study supports the real-world use of these procedures or their modifications. By improving tiny neural networks’ performance, medical diagnostics, signal reconstruction, and picture processing might become more accurate and precise.