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1. Activity recognition in wearables using adversarial multi-source domain adaptation

   https://doi.org/https://doi.org/10.1016/j.smhl.2020.100174  

   Chakma, A; Faridee, A; Khan, H; Roy, N (March 2021, Smart health)

   null (Ed.)

   Human activity recognition (HAR) from wearable sensors data has become ubiquitous due to the widespread proliferation of IoT and wearable devices. However, recognizing human activity in heterogeneous environments, for example, with sensors of different models and make, across different persons and their on-body sensor placements introduces wide range discrepancies in the data distributions, and therefore, leads to an increased error margin. Transductive transfer learning techniques such as domain adaptation have been quite successful in mitigating the domain discrepancies between the source and target domain distributions without the costly target domain data annotations. However, little exploration has been done when multiple distinct source domains are present, and the optimum mapping to the target domain from each source is not apparent. In this paper, we propose a deep Multi-Source Adversarial Domain Adaptation (MSADA) framework that opportunistically helps select the most relevant feature representations from multiple source domains and establish such mappings to the target domain by learning the perplexity scores. We showcase that the learned mappings can actually reflect our prior knowledge on the semantic relationships between the domains, indicating that MSADA can be employed as a powerful tool for exploratory activity data analysis. We empirically demonstrate that our proposed multi-source domain adaptation approach achieves 2% improvement with OPPORTUNITY dataset (cross-person heterogeneity, 4 ADLs), whereas 13% improvement on DSADS dataset (cross-position heterogeneity, 10 ADLs and sports activities). 

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2. A Framework for Few-Shot Policy Transfer through Observation Mapping and Behavior Cloning

   https://doi.org/10.1109/IROS55552.2023.10342477  

   Shukla, Y.; Kesari, B.; Goel, S.; Wright, R.; Sinapov, J. (November 2023, Proceedings of the IEEERSJ International Conference on Intelligent Robots and Systems)

   Abstract— Despite recent progress in Reinforcement Learning for robotics applications, many tasks remain prohibitively difficult to solve because of the expensive interaction cost. Transfer learning helps reduce the training time in the target domain by transferring knowledge learned in a source domain. Sim2Real transfer helps transfer knowledge from a simulated robotic domain to a physical target domain. Knowledge transfer reduces the time required to train a task in the physical world, where the cost of interactions is high. However, most existing approaches assume exact correspondence in the task structure and the physical properties of the two domains. This work proposes a framework for Few-Shot Policy Transfer between two domains through Observation Mapping and Behavior Cloning. We use Generative Adversarial Networks (GANs) along with a cycle-consistency loss to map the observations between the source and target domains and later use this learned mapping to clone the successful source task behavior policy to the target domain. We observe successful behavior policy transfer with limited target task interactions and in cases where the source and target task are semantically dissimilar. 

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3. Data-Driven Approach to Multiple-Source Domain Adaptation

   Petar Stojanov, Mingming Gong (April 2019, Proceedings of Machine Learning Research)

   A key problem in domain adaptation is determining what to transfer across different domains. We propose a data-driven method to represent these changes across multiple source domains and perform unsupervised domain adaptation. We assume that the joint distributions follow a specific generating process and have a small number of identifiable changing parameters, and develop a data-driven method to identify the changing parameters by learning low-dimensional representations of the changing class-conditional distributions across multiple source domains. The learned low-dimensional representations enable us to reconstruct the target-domain joint distribution from unlabeled target-domain data, and further enable predicting the labels in the target domain. We demonstrate the efficacy of this method by conducting experiments on synthetic and real datasets. 

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4. Multi-source Domain Adaptation for Semantic Segmentation”, Advances in Neural Information Processing Systems

   Zhao, S.; Li, B.; Yue, X.; Gu, Y.; Xu, P.; Hu, R.; Chai, H.; Keutzer, K. (October 2019, Advances in neural information processing systems)

   Simulation-to-real domain adaptation for semantic segmentation has been actively studied for various applications such as autonomous driving. Existing methods mainly focus on a single-source setting, which cannot easily handle a more practical scenario of multiple sources with different distributions. In this paper, we propose to investigate multi-source domain adaptation for semantic segmentation. Specifically, we design a novel framework, termed Multi-source Adversarial Domain Aggregation Network (MADAN), which can be trained in an end-to-end manner. First, we generate an adapted domain for each source with dynamic semantic consistency while aligning at the pixel-level cycle-consistently towards the target. Second, we propose sub-domain aggregation discriminator and cross-domain cycle discriminator to make different adapted domains more closely aggregated. Finally, feature-level alignment is performed between the aggregated domain and target domain while training the segmentation network. Extensive experiments from synthetic GTA and SYNTHIA to real Cityscapes and BDDS datasets demonstrate that the proposed MADAN model outperforms state-of-the-art approaches. Our source code is released at: https://github.com/Luodian/MADAN. 

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5. Energy-based Domain Adaption with Active Learning for Emerging Misinformation Detection

   https://doi.org/10.1109/BigData55660.2022.10021038  

   Lee, Kyumin; Mou, Guanyi; Sievert, Scott (December 2022, EEE International Conference on Big Data (Big Data))

   Classifying whether collected information related to emerging topics and domains is fake/incorrect is not an easy task because we do not have enough labeled data in the domains. Given labeled data from source domains (e.g., gossip and health) and limited labeled data from a newly emerging target domain (e.g., COVID-19 and Ukraine war), simply applying knowledge learned from source domains to the target domain may not work well because of different data distribution. To solve the problem, in this paper, we propose an energy-based domain adaptation with active learning for early misinformation detection. Given three real world news datasets, we evaluate our proposed model against two baselines in both domain adaptation and the whole pipeline. Our model outperforms the baselines, improving at least 5% in the domain adaptation task and 10% in the whole pipeline, showing effectiveness of our proposed approach. 

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