Unsupervised Domain Adaptation by Backpropagation (1409.7495v2)

Abstract: Top-performing deep architectures are trained on massive amounts of labeled data. In the absence of labeled data for a certain task, domain adaptation often provides an attractive option given that labeled data of similar nature but from a different domain (e.g. synthetic images) are available. Here, we propose a new approach to domain adaptation in deep architectures that can be trained on large amount of labeled data from the source domain and large amount of unlabeled data from the target domain (no labeled target-domain data is necessary). As the training progresses, the approach promotes the emergence of "deep" features that are (i) discriminative for the main learning task on the source domain and (ii) invariant with respect to the shift between the domains. We show that this adaptation behaviour can be achieved in almost any feed-forward model by augmenting it with few standard layers and a simple new gradient reversal layer. The resulting augmented architecture can be trained using standard backpropagation. Overall, the approach can be implemented with little effort using any of the deep-learning packages. The method performs very well in a series of image classification experiments, achieving adaptation effect in the presence of big domain shifts and outperforming previous state-of-the-art on Office datasets.

• The paper introduces a gradient reversal layer (GRL) that embeds unsupervised domain adaptation into standard backpropagation training.
• The paper demonstrates significant accuracy improvements across various benchmarks, such as boosting MNIST-M accuracy from 57.49% to 81.49%.
• The paper’s approach reduces the need for labeled target data by leveraging large unlabeled datasets alongside existing deep learning pipelines.

Unsupervised Domain Adaptation by Backpropagation

This paper, authored by Yaroslav Ganin and Victor Lempitsky, introduces a novel approach for unsupervised domain adaptation within deep learning architectures. This method enables effective training on extensive labeled data from a source domain and extensive unlabeled data from a target domain, with no labeled data required from the target domain. The primary innovation lies in embedding domain adaptation into the learning representation process through a gradient reversal layer, allowing for standard backpropagation training.

Methodology

The authors propose an augmented feed-forward network architecture comprising three main components:

1. Feature Extractor: A deep learning component that maps input data to a feature space.
2. Label Predictor: A classifier operating on the feature space to predict class labels.
3. Domain Classifier: A classifier that discriminates between the source and target domains.

Central to the approach is the gradient reversal layer (GRL), strategically placed between the feature extractor and the domain classifier. The GRL acts as an identity function during forward propagation but reverses the gradient's sign during backpropagation. This reversal encourages the extracted features to be both discriminative (for accurate label prediction) and invariant to domain shifts (for effective domain adaptation). The training objective is to minimize the label prediction error while maximizing the domain classification error, promoting feature invariance across domains.

Numerical Results and Experiments

The efficacy of this approach is showcased through extensive experiments on various image datasets, notably:

• MNIST to MNIST-M: Achieved significant accuracy improvement from 57.49% to 81.49%.
• Synthetic Numbers to SVHN: Improved accuracy from 86.65% to 90.48%.
• SVHN to MNIST: Reported an improvement from 59.19% to 71.07%.
• Synthetic Signs to GTSRB: Enhanced accuracy from 74.00% to 88.66%.

Additionally, the method was tested on the Office dataset, a standard benchmark for domain adaptation. The results showed considerable improvements over previous state-of-the-art methods:

• Amazon to Webcam: Achieved 67.3% accuracy.
• DSLR to Webcam: Reached 94.0% accuracy.
• Webcam to DSLR: Attained 93.7% accuracy.

Implications and Future Directions

The proposed method facilitates robust domain adaptation with minimal changes to existing deep learning pipelines. It is particularly advantageous in scenarios where labeled data is unavailable for the target domain but ample unlabeled data exists. Practically, this can significantly reduce the labeling effort and leverage abundant synthetic data for training robust models applicable to real-world tasks.

From a theoretical standpoint, the approach offers insights into embedding domain adaptation into the learning process, making the features domain-invariant and preserving discriminative power. Incorporating labeled data from the target domain in semi-supervised settings further enhances performance, as demonstrated in additional experiments.

Future research could focus on refining the GRL and exploring its integration with other domain adaptation techniques such as adversarial training. Moreover, expanding this framework to handle multi-domain adaptation and examining its application to more complex tasks and diverse data types will be valuable. The potential for initializing the feature extractor with pre-trained models or autoencoders also presents an intriguing avenue for enhancing performance further.

In summary, this paper provides a solid and practical solution for unsupervised domain adaptation that integrates smoothly with standard deep learning training protocols, offering substantial improvements in various image classification benchmarks.