Generate To Adapt: Aligning Domains using Generative Adversarial Networks (1704.01705v4)

Abstract: Domain Adaptation is an actively researched problem in Computer Vision. In this work, we propose an approach that leverages unsupervised data to bring the source and target distributions closer in a learned joint feature space. We accomplish this by inducing a symbiotic relationship between the learned embedding and a generative adversarial network. This is in contrast to methods which use the adversarial framework for realistic data generation and retraining deep models with such data. We demonstrate the strength and generality of our approach by performing experiments on three different tasks with varying levels of difficulty: (1) Digit classification (MNIST, SVHN and USPS datasets) (2) Object recognition using OFFICE dataset and (3) Domain adaptation from synthetic to real data. Our method achieves state-of-the art performance in most experimental settings and by far the only GAN-based method that has been shown to work well across different datasets such as OFFICE and DIGITS.

Analysis of "Generate To Adapt: Aligning Domains using Generative Adversarial Networks"

The paper "Generate To Adapt: Aligning Domains using Generative Adversarial Networks" addresses the challenge of unsupervised domain adaptation in computer vision, focusing on aligning source and target distributions within a learned joint feature space. This is achieved through a unique application of Generative Adversarial Networks (GANs), distinct from traditional methods that generate realistic data for model retraining.

Methodology

The authors propose a dual-stream framework comprising a classification branch and an adversarial branch using an Auxiliary Classifier GAN (ACGAN) framework. The architecture is explicitly designed to learn embeddings robust to domain shifts by leveraging both supervised source domain labels and unsupervised target data. The adversarial approach involves learning to generate source-like images from embedding spaces, thus reducing distributional shifts.

Key Elements:

1. Auxiliary Classifier GAN (ACGAN): The GAN framework is employed not for data augmentation but to induce a rich gradient flow for learning domain-invariant embeddings. The discriminator functions not only as a real/fake classifier but as a multi-class classifier for the source domain.
2. Iterative Optimization: The optimization alternates between updating the discriminator, generator, and feature extraction networks, facilitating continuous adaptation of the embeddings to minimize the domain gap.
3. Dual Stream Training: During training, the discriminator receives input from both real and generated images, while the generator is guided by the need to create class-consistent and realistic images.

Experiments

The effectiveness of the proposed method is validated across various domain adaptation challenges, with superior results particularly notable in the following settings:

• Digit Classification: Achieves high accuracy in tasks such as MNIST to USPS adaptation and shows significant performance improvements over existing methods in SVHN to MNIST adaptation.
• OFFICE Dataset: Demonstrates robust performance on a dataset characterized by small sample sizes and complex distributions, illustrating the approach's versatility.
• Synthetic to Real: Confronts the entirely synthetic CAD dataset's transfer to the PASCAL dataset, managing high domain shifts and achieving notable accuracy improvements.

Results and Implications

In the domain adaptation landscape, the proposed approach establishes itself as a robust alternative by directly addressing distribution shifts in the feature space, rather than relying solely on image-to-image translation as in previous GAN-based methods. This methodology shows effectiveness even under substantial domain disparities, such as synthetic to real transitions.

Future Directions

The research points towards potential applications in fields where labeled data is scarce, like medical imaging or robotics. Further exploration into more complex network architectures for both generators and feature extractors could lead to enhanced adaptability and accuracy.

The implications of this work advocate for continued research into joint adversarial-discriminative frameworks. The approach extends beyond the field of mere dataset augmentation, challenging traditional paradigms of domain adaptation and potentially prompting new developments in AI systems' learning and adaptation capabilities.