Incremental Adversarial Domain Adaptation for Continually Changing Environments

Continuous appearance shifts such as changes in weather and lighting conditions can impact the performance of deployed machine learning models. While unsupervised domain adaptation aims to address this challenge, current approaches do not utilise the continuity of the occurring shifts. In particular, many robotics applications exhibit these conditions and thus facilitate the potential to incrementally adapt a learnt model over minor shifts which integrate to massive differences over time. Our work presents an adversarial approach for lifelong, incremental domain adaptation which benefits from unsupervised alignment to a series of intermediate domains which successively diverge from the labelled source domain. We empirically demonstrate that our incremental approach improves handling of large appearance changes, e.g. day to night, on a traversable-path segmentation task compared with a direct, single alignment step approach. Furthermore, by approximating the feature distribution for the source domain with a generative adversarial network, the deployment module can be rendered fully independent of retaining potentially large amounts of the related source training data for only a minor reduction in performance.Comment: International Conference on Robotics and Automation 201

Dynamic Adaptation on Non-Stationary Visual Domains

Domain adaptation aims to learn models on a supervised source domain that perform well on an unsupervised target. Prior work has examined domain adaptation in the context of stationary domain shifts, i.e. static data sets. However, with large-scale or dynamic data sources, data from a defined domain is not usually available all at once. For instance, in a streaming data scenario, dataset statistics effectively become a function of time. We introduce a framework for adaptation over non-stationary distribution shifts applicable to large-scale and streaming data scenarios. The model is adapted sequentially over incoming unsupervised streaming data batches. This enables improvements over several batches without the need for any additionally annotated data. To demonstrate the effectiveness of our proposed framework, we modify associative domain adaptation to work well on source and target data batches with unequal class distributions. We apply our method to several adaptation benchmark datasets for classification and show improved classifier accuracy not only for the currently adapted batch, but also when applied on future stream batches. Furthermore, we show the applicability of our associative learning modifications to semantic segmentation, where we achieve competitive results

DAugNet: Unsupervised, Multi-source, Multi-target, and Life-long Domain Adaptation for Semantic Segmentation of Satellite Images

The domain adaptation of satellite images has recently gained an increasing attention to overcome the limited generalization abilities of machine learning models when segmenting large-scale satellite images. Most of the existing approaches seek for adapting the model from one domain to another. However, such single-source and single-target setting prevents the methods from being scalable solutions, since nowadays multiple source and target domains having different data distributions are usually available. Besides, the continuous proliferation of satellite images necessitates the classifiers to adapt to continuously increasing data. We propose a novel approach, coined DAugNet, for unsupervised, multi-source, multi-target, and life-long domain adaptation of satellite images. It consists of a classifier and a data augmentor. The data augmentor, which is a shallow network, is able to perform style transfer between multiple satellite images in an unsupervised manner, even when new data are added over the time. In each training iteration, it provides the classifier with diversified data, which makes the classifier robust to large data distribution difference between the domains. Our extensive experiments prove that DAugNet significantly better generalizes to new geographic locations than the existing approaches

Adjusting bass and treble: the continuously modulated performance of gender

My research into domestic labour in same sex households has prompted me to radically rethink my ideas about gender. Most work on the domestic division of labour has found that the family-household is one of the key sites for the maintenance and reproduction of the gendered division of labour and, in fact, the production of men’s and women’s gendered identities (Baxter 1993, Stacey 1991, Morris 1990, Delphy and Leonard 1992, Ferree 1990). As the partners in a same sex relationship are by definition of the same gender, I initially supposed I would need to find some other theoretical perspective on the dynamics of domestic labour to underpin my analysis. However, as I conducted the interviews, it seemed to me that each of the respondents performed their gender in different ways to their partner, and indeed to all the other respondents, and this seemed to involve a subtle and complex interplay of many endogenous, contextual and relational factors. Bem’s (1995) article on dismantling gender polarization by turning the volume of gender difference down or up suggested a useful analogy to portray a more nuanced understanding of this dynamic, along the lines of adjusting the bass and treble controls of a sound system – hence the continuously modulated performance of gender. Building on Connell’s (1987) understanding of the multiple forms of masculinity performed by different groups of men, in this paper I wish to explore the idea that each person performs different degrees of masculinity and femininity simultaneously, in the context of different domains of social and cultural space, and in relation to other actors in that space. Finally I have used Bourdieu’s ideas about habitus, field, and reflexivity to explore how people may engage with this process in a more or less conscious manner, and with differing degrees of complexity and skill. I hope in this way to contribute a slightly different perspective to the understanding of gender as performatively constituted, and perhaps also to furnish the ‘social imaginary’ (Taylor 2002) with some new ways to think about gender

Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective

This paper takes a problem-oriented perspective and presents a comprehensive review of transfer learning methods, both shallow and deep, for cross-dataset visual recognition. Specifically, it categorises the cross-dataset recognition into seventeen problems based on a set of carefully chosen data and label attributes. Such a problem-oriented taxonomy has allowed us to examine how different transfer learning approaches tackle each problem and how well each problem has been researched to date. The comprehensive problem-oriented review of the advances in transfer learning with respect to the problem has not only revealed the challenges in transfer learning for visual recognition, but also the problems (e.g. eight of the seventeen problems) that have been scarcely studied. This survey not only presents an up-to-date technical review for researchers, but also a systematic approach and a reference for a machine learning practitioner to categorise a real problem and to look up for a possible solution accordingly

Neurogenesis Deep Learning

Neural machine learning methods, such as deep neural networks (DNN), have achieved remarkable success in a number of complex data processing tasks. These methods have arguably had their strongest impact on tasks such as image and audio processing - data processing domains in which humans have long held clear advantages over conventional algorithms. In contrast to biological neural systems, which are capable of learning continuously, deep artificial networks have a limited ability for incorporating new information in an already trained network. As a result, methods for continuous learning are potentially highly impactful in enabling the application of deep networks to dynamic data sets. Here, inspired by the process of adult neurogenesis in the hippocampus, we explore the potential for adding new neurons to deep layers of artificial neural networks in order to facilitate their acquisition of novel information while preserving previously trained data representations. Our results on the MNIST handwritten digit dataset and the NIST SD 19 dataset, which includes lower and upper case letters and digits, demonstrate that neurogenesis is well suited for addressing the stability-plasticity dilemma that has long challenged adaptive machine learning algorithms.Comment: 8 pages, 8 figures, Accepted to 2017 International Joint Conference on Neural Networks (IJCNN 2017

{SHIFT}: {A} Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation

Adapting to a continuously evolving environment is a safety-critical challenge inevitably faced by all autonomous-driving systems. Existing image- and video-based driving datasets, however, fall short of capturing the mutable nature of the real world. In this paper, we introduce the largest multi-task synthetic dataset for autonomous driving, SHIFT. It presents discrete and continuous shifts in cloudiness, rain and fog intensity, time of day, and vehicle and pedestrian density. Featuring a comprehensive sensor suite and annotations for several mainstream perception tasks, SHIFT allows to investigate how a perception systems' performance degrades at increasing levels of domain shift, fostering the development of continuous adaptation strategies to mitigate this problem and assessing the robustness and generality of a model. Our dataset and benchmark toolkit are publicly available at www.vis.xyz/shift

Online Domain Adaptation for Multi-Object Tracking

Automatically detecting, labeling, and tracking objects in videos depends first and foremost on accurate category-level object detectors. These might, however, not always be available in practice, as acquiring high-quality large scale labeled training datasets is either too costly or impractical for all possible real-world application scenarios. A scalable solution consists in re-using object detectors pre-trained on generic datasets. This work is the first to investigate the problem of on-line domain adaptation of object detectors for causal multi-object tracking (MOT). We propose to alleviate the dataset bias by adapting detectors from category to instances, and back: (i) we jointly learn all target models by adapting them from the pre-trained one, and (ii) we also adapt the pre-trained model on-line. We introduce an on-line multi-task learning algorithm to efficiently share parameters and reduce drift, while gradually improving recall. Our approach is applicable to any linear object detector, and we evaluate both cheap "mini-Fisher Vectors" and expensive "off-the-shelf" ConvNet features. We quantitatively measure the benefit of our domain adaptation strategy on the KITTI tracking benchmark and on a new dataset (PASCAL-to-KITTI) we introduce to study the domain mismatch problem in MOT.Comment: To appear at BMVC 201

Shaping Giant Membrane Vesicles in 3D-Printed Protein Hydrogel Cages

Giant unilamellar phospholipid vesicles are attractive starting points for constructing minimal living cells from the bottom-up. Their membranes are compatible with many physiologically functional modules and act as selective barriers, while retaining a high morphological flexibility. However, their spherical shape renders them rather inappropriate to study phenomena that are based on distinct cell shape and polarity, such as cell division. Here, a microscale device based on 3D printed protein hydrogel is introduced to induce pH-stimulated reversible shape changes in trapped vesicles without compromising their free-standing membranes. Deformations of spheres to at least twice their aspect ratio, but also toward unusual quadratic or triangular shapes can be accomplished. Mechanical force induced by the cages to phase-separated membrane vesicles can lead to spontaneous shape deformations, from the recurrent formation of dumbbells with curved necks between domains to full budding of membrane domains as separate vesicles. Moreover, shape-tunable vesicles are particularly desirable when reconstituting geometry-sensitive protein networks, such as reaction-diffusion systems. In particular, vesicle shape changes allow to switch between different modes of self-organized protein oscillations within, and thus, to influence reaction networks directly by external mechanical cues