# Enhanced Transfer Learning with ImageNet Trained Classification Layer

## TL;DR

This paper investigates the impact of using the ImageNet pre-trained classification layer during transfer learning, showing that including it can improve fine-tuning performance and network adaptability.

## Contribution

It introduces a layer-wise fine-tuning method that incorporates the pre-trained classification layer, revealing its less category-specific nature and importance for better transfer learning.

## Key findings

- Proposed fine-tuning outperforms traditional methods.
- Pre-trained classification layer contains more global information.
- Normalization and scaling are crucial for domain adaptation.

## Abstract

Parameter fine tuning is a transfer learning approach whereby learned parameters from pre-trained source network are transferred to the target network followed by fine-tuning. Prior research has shown that this approach is capable of improving task performance. However, the impact of the ImageNet pre-trained classification layer in parameter fine-tuning is mostly unexplored in the literature. In this paper, we propose a fine-tuning approach with the pre-trained classification layer. We employ layer-wise fine-tuning to determine which layers should be frozen for optimal performance. Our empirical analysis demonstrates that the proposed fine-tuning performs better than traditional fine-tuning. This finding indicates that the pre-trained classification layer holds less category-specific or more global information than believed earlier. Thus, we hypothesize that the presence of this layer is crucial for growing network depth to adapt better to a new task. Our study manifests that careful normalization and scaling are essential for creating harmony between the pre-trained and new layers for target domain adaptation. We evaluate the proposed depth augmented networks for fine-tuning on several challenging benchmark datasets and show that they can achieve higher classification accuracy than contemporary transfer learning approaches.