text: Deep neural networks have demonstrated impressive performance in various machine learning tasks. However, they are notoriously sensitive to changes in data distribution. Often, even a slight change in the distribution can lead to drastic performance reduction. Artificially augmenting the data may help to some extent, but in most cases, fails to achieve model invariance to the data distribution. Some examples where this sub-class of domain adaptation can be valuable are various imaging modalities such as thermal imaging, X-ray, ultrasound, and MRI, where changes in acquisition parameters or acquisition device manufacturer will result in different representation of the same input. Our work shows that standard finetuning fails to adapt the model in certain important cases. We propose a novel method of adapting to a new data source, and demonstrate near perfect adaptation on a customized ImageNet benchmark.
  @cite_0: 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.
  @cite_4: Recent works showed that Generative Adversarial Networks (GANs) can be successfully applied in unsupervised domain adaptation, where, given a labeled source dataset and an unlabeled target dataset, the goal is to train powerful classifiers for the target samples. In particular, it was shown that a GAN objective function can be used to learn target features indistinguishable from the source ones. In this work, we extend this framework by (i) forcing the learned feature extractor to be domain-invariant, and (ii) training it through data augmentation in the feature space, namely performing feature augmentation. While data augmentation in the image space is a well established technique in deep learning, feature augmentation has not yet received the same level of attention. We accomplish it by means of a feature generator trained by playing the GAN minimax game against source features. Results show that both enforcing domain-invariance and performing feature augmentation lead to superior or comparable performance to state-of-the-art results in several unsupervised domain adaptation benchmarks.
  @cite_7: Appearance changes due to weather and seasonal conditions represent a strong impediment to the robust implementation of machine learning systems in outdoor robotics. While the model is optimised for the training domain it will deliver degraded performance in application domains that underlie distributional shifts caused by these changes. Traditionally, this problem has been addressed via the collection of labelled data in multiple domains or by imposing priors on the type of shift between both domains. We frame the problem in the context of unsupervised domain adaptation and apply an adversarial framework to train a deep neural network with the additional objective to align features across domains. This approach benefits from adding unlabelled data and is generally applicable to many state-of-the-art architectures. Moreover, as adversarial training is notoriously hard to stabilise, we first perform an extensive ablation study on a surrogate classification task underlying the same appearance change and then apply the distilled insights to the problem of free-space segmentation for motion planning.
  @cite_11: 
summary: Recently, domain adaptation methods inspired by generative adversarial networks (GAN) @cite_11 have gained popularity. For example, @cite_0 added a domain discriminator to enforce domain invariance of the extracted features. It can be seen as two agents competing in a minmax game. One agent is trying to minimize classification error and maximize discriminator domain confusion. Another agent is trying to minimize discriminator domain confusion. Work by @cite_7 use this technique to overcome different outdoor lighting conditions between source and destination domains. @cite_4 add feature augmentation on top of the domain discriminator. GAN inspired methods differ from our approach, as our approach which does not involve a discriminator and or generator. GANs are also notoriously hard to train. Despite the development of techniques to improve the training process, gradient explosion and mode collapse are just a few of the many common practical issues faced when trying to train a GAN-based model.