Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-tuning (1702.08690v2)

Abstract: Deep neural networks require a large amount of labeled training data during supervised learning. However, collecting and labeling so much data might be infeasible in many cases. In this paper, we introduce a source-target selective joint fine-tuning scheme for improving the performance of deep learning tasks with insufficient training data. In this scheme, a target learning task with insufficient training data is carried out simultaneously with another source learning task with abundant training data. However, the source learning task does not use all existing training data. Our core idea is to identify and use a subset of training images from the original source learning task whose low-level characteristics are similar to those from the target learning task, and jointly fine-tune shared convolutional layers for both tasks. Specifically, we compute descriptors from linear or nonlinear filter bank responses on training images from both tasks, and use such descriptors to search for a desired subset of training samples for the source learning task. Experiments demonstrate that our selective joint fine-tuning scheme achieves state-of-the-art performance on multiple visual classification tasks with insufficient training data for deep learning. Such tasks include Caltech 256, MIT Indoor 67, Oxford Flowers 102 and Stanford Dogs 120. In comparison to fine-tuning without a source domain, the proposed method can improve the classification accuracy by 2% - 10% using a single model.

• The paper’s main contribution is the selective joint fine-tuning method that strategically leverages source data to boost CNN performance in data-limited tasks.
• The methodology fine-tunes shared convolutional layers using low-level feature similarities derived from descriptors like Gabor filters and pre-trained CNN kernels.
• Experiments on datasets such as Caltech 256 and Stanford Dogs show accuracy improvements of 2% to 10% over traditional fine-tuning approaches.

Deep Transfer Learning through Selective Joint Fine-Tuning: An Expert Overview

The paper "Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-Tuning" by Weifeng Ge and Yizhou Yu presents a novel approach to improve the performance of deep convolutional neural networks (CNNs) on tasks with insufficient training data using a deep transfer learning methodology. The proposed scheme, named selective joint fine-tuning, couples a target learning task with another source learning task rich in labeled data, while carefully selecting relevant training samples for effective knowledge transfer.

Methodological Insights

Deep transfer learning has been leveraged to address the scarcity of labeled data in training large-scale neural networks. The innovative aspect of this approach is the selective retrieval of training images from a source task that exhibits similar low-level characteristics to those of the target task. This strategic selection is performed using descriptors derived from linear or nonlinear filter bank responses, such as Gabor filters or the kernels from pre-trained layers of deep CNNs, like AlexNet. The method allows selective fine-tuning of shared convolutional layers, thus mitigating overfitting and enhancing the network's ability to generalize for the target task.

Experimental Evaluation

The efficacy of this approach is rigorously evaluated across multiple visual classification challenges. It shows notable improvements in classification tasks with limited training datasets such as Caltech 256, MIT Indoor 67, and fine-grained classification datasets like Oxford Flowers 102 and Stanford Dogs 120. The scheme improves classification accuracy by 2% to 10% over conventional fine-tuning without a source domain, which is remarkable given the challenges of training deep networks with limited data. These results highlight the potential of selective joint fine-tuning to leverage large-scale datasets for effectively training models on specialized tasks.

Implications and Future Directions

The research introduces a methodology that broadens the scope of transfer learning by focusing on the alignment of low-level image characteristics between source and target domains. This is a significant extension in the field of transfer learning, impacting theoretical perspectives on domain adaptation and multi-task learning. The enhanced ability of networks to learn discriminative features with fewer data suggests operational benefits for practical applications where data collection and annotation are constrained.

Looking forward, future work could explore algorithmic strategies for optimal source domain selection tailored to specific target tasks, as well as further enhancements to the selection mechanism to capture more nuanced feature correspondences. Additionally, expanding the methodology to other domains beyond image classification could unlock new applications and deepen our understanding of transfer learning dynamics.

In conclusion, the selective joint fine-tuning technique presents a compelling strategy for dealing with data-poor environments in deep learning, reinforcing the importance of transfer learning in modern AI research directions. The results and insights provided by this work form a robust platform for future exploration in efficient and effective deep network training with minimal labeled data.