Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks (1612.05424v2)

Abstract: Collecting well-annotated image datasets to train modern machine learning algorithms is prohibitively expensive for many tasks. One appealing alternative is rendering synthetic data where ground-truth annotations are generated automatically. Unfortunately, models trained purely on rendered images often fail to generalize to real images. To address this shortcoming, prior work introduced unsupervised domain adaptation algorithms that attempt to map representations between the two domains or learn to extract features that are domain-invariant. In this work, we present a new approach that learns, in an unsupervised manner, a transformation in the pixel space from one domain to the other. Our generative adversarial network (GAN)-based method adapts source-domain images to appear as if drawn from the target domain. Our approach not only produces plausible samples, but also outperforms the state-of-the-art on a number of unsupervised domain adaptation scenarios by large margins. Finally, we demonstrate that the adaptation process generalizes to object classes unseen during training.

Unsupervised Pixel-Level Domain Adaptation With Generative Adversarial Networks

The paper, "Unsupervised Pixel-Level Domain Adaptation With Generative Adversarial Networks," investigates the challenge of transferring machine learning models trained on synthetic data to real-world applications. This task, known as domain adaptation, is particularly pertinent in computer vision, where labeled data is expensive and time-consuming to annotate. Instead, leveraging synthetic datasets where annotations are readily available offers a cost-effective solution. However, models trained on synthetic data often fall short when generalizing to real-world images, necessitating efficient domain adaptation techniques.

Approach

The authors present a novel unsupervised domain adaptation methodology leveraging Generative Adversarial Networks (GANs). Unlike traditional approaches that adapt representations or learn domain-invariant features, this approach, termed PixelDA, focuses on transforming images at the pixel level from the source domain to resemble those from the target domain. This adaptation occurs through GANs without any paired correspondence between source and target domain images and incorporates several advantageous features:

1. Decoupling from Task-Specific Architecture: This method allows the domain adaptation to be independent of the task-specific architecture, promoting flexibility in the model choice for subsequent tasks.
2. Generalization Across Label Spaces: The model is capable of generalizing across domains even if the label spaces differ between training and testing.
3. Training Stability: The integration of a task-specific loss and pixel similarity regularization stabilizes the training process, reducing the sensitivity to random initialization.
4. Data Augmentation: It enhances the dataset by generating stochastic samples that resemble target domain images.
5. Interpretability: The domain-adapted images provide more interpretable outputs than domain-adapted feature vectors.

Methodology

The proposed framework employs a generator $G$ to transform a source image $x^s$ and noise vector $z$ into an adapted image $x^f$. A discriminator $D$ is tasked with distinguishing between real images from the target domain and fake, generated images. Alongside, a task-specific classifier $T$ predicts class labels $\hat{y}$ based on the generated images. These components are trained via a minimax game leveraging a GAN-based objective and incorporating additional task-specific and pixel-level similarity losses to ensure the preservation of essential content and stability in training.

Evaluation

Evaluation spans several domain adaptation scenarios: converting MNIST to USPS, MNIST to MNIST-M, and synthetic to real images in the Cropped LineMod dataset. The results demonstrate that PixelDA outperforms state-of-the-art techniques in classification tasks on MNIST and USPS and significantly reduces pose estimation errors in the LineMod dataset.

Quantitative Results

The quantitative evaluations reveal:

• For the MNIST to USPS scenario, PixelDA achieves 95.9% accuracy, outperforming previous models like DSN (91.3%).
• In the MNIST to MNIST-M scenario, it achieves 98.2% accuracy, surpassing the prior best of 83.2% by DSN and even exceeding the "Target-only" model.
• Within the Synthetic Cropped LineMod to Cropped LineMod scenario, PixelDA reduces the mean angle error for pose estimation significantly, from 56.58° (DANN) to 23.5°.

Qualitative Results

Qualitative analysis includes visual assessments, where generated images are compared to their nearest neighbors in the target domain, affirming that PixelDA generates realistic and domain-specific images without overfitting to the training samples.

Implications

Practically, PixelDA offers substantial improvements in the adaptability of vision-based models from synthetic to real domains, facilitating applications in areas that rely heavily on synthetic data, including robotics and simulation-based learning environments. Theoretically, the model strengthens the understanding of pixel-level transformations and extends the capability of GANs to more complex, unsupervised domain adaptation tasks.

Future Directions

Future work may explore:

• Extending the approach to more complex datasets and tasks beyond image classification and pose estimation.
• Enhancing scalability and efficiency for higher resolution images.
• Investigating the integration of additional modalities such as depth and semantic information for richer domain adaptation.
• Formalizing the theoretical foundations of pixel-level adaptation to provide deeper insights into the transformation mechanics.

The paper demonstrates significant strides in unsupervised domain adaptation, marking an essential step towards robust and flexible machine learning models that transcend domain constraints.