A Comprehensive Analysis of Deep Regression.

Area of Science:

• Computer Vision
• Machine Learning
• Data Science

Background:

• Deep learning's popularity has surged, leading to numerous deep network applications in vision tasks like human pose estimation.
• Variations in network architecture, data pre-processing, and optimization create performance differences, complicating method comparison.

Purpose of the Study:

• To systematically evaluate and statistically analyze deep regression techniques, specifically convolutional neural networks with linear regression top layers.
• To provide a comprehensive analysis of deep regression for vision problems, reporting confidence intervals and statistical significance.

Main Methods:

• Conducted experiments on four distinct vision problems using convolutional neural networks with linear regression.
• Performed systematic evaluation and statistical analysis, including confidence intervals for median performance.
• Investigated the impact of data pre-processing and network architecture modifications on regression performance.

Main Results:

• Variability in results was predominantly driven by data pre-processing methods, often overshadowing architectural changes.
• General-purpose networks (e.g., VGG-16, ResNet-50) with proper tuning achieved performance comparable to state-of-the-art.
• Statistical significance was reported for observed performance differences, where applicable.

Conclusions:

• Data pre-processing is a critical factor in deep regression performance for vision tasks.
• Complex, ad-hoc regression models are often unnecessary; adequately tuned general-purpose networks can be highly effective.