Although the market valuation and adoption of IoT in various sectors is in an uptrend, the actual deployment is lagging when compared to the industrial forecasted data. The main reason behind this drawback is the lifespan of IoT devices due to their limited battery capacities. A solution to the problem is to deploy energy harvesting units (e.g., solar to replenish the batteries). However, due to the time varying availability of both irradiance and temperature and their effect on the power output, it is essential to predict both variables. To this end, we propose in this paper a prediction system that does not consume a lot of energy and that can be deployed on low computational nodes. This model consists of a Seasonal Auto Regressive Integrated Moving Average (SARIMA) with a Kalman filtering (KF) component. We build this model using an actual dataset for Ottawa, Ontario, Canada. We then demonstrate its effectiveness by presenting the results for randomly selected days in the Winter Season. In this context, we show that the SARIMA-KF outperforms the SARIMA in all scenarios with an average error reduction of 59.3%.

One way of addressing the problem of unsupervised domain adaptation (UDA) is to perform adversarial training between two classifiers and their shared feature extractor. The two classifiers are enforced to detect the misaligned regions between the source and target domains, while the feature extractor aligns the features by confusing the classifiers. Although this method yields improvement, it ignores the relationship among target neighbors, which may consequently limit the model performance. In this work, we propose a new alignment strategy based on the "cluster assumption" to ensure the aligned target features preserve their clusters by avoiding overlap with decision boundaries. Furthermore, to make the aligned features more compact, we constrain them to be ro-bust against adversarial perturbation using the different views of the classifiers. Extensive experiments demonstrate the effectiveness of our solution on various datasets.

Recent approaches to unsupervised domain adaptation focus on transferring knowledge from source (labeled) data to target (unlabeled) data. Both data types share the same class space but originate from different domains. One way to achieve this transfer is tasking two classifiers to detect target features that diverge from source features. Meanwhile, a feature generator is adversarially refined to match the detected features with the source ones. However, the aligned features are still subject to ambiguity. Samples that are not smoothly distributed on the latent manifold are often missed in training. Moreover, target data may not be sufficient for adversarial learning. To overcome these problems, our proposed Adversarially Smoothed Feature Alignment (AdvSFA) model is designed to identify ambiguous target inputs by maximizing classifiers discrepancy in an extended class space. This enables the generator to receive valuable feedback from the classifiers and consequently learn more discriminative and smooth representation. Imposing smoothness on the latent manifold is a desirable property to improve model generalization and avoid having neighboring samples of different classes. To further promote such property, we not only task the generator to conduct feature alignment on target examples and but also in-between them. By adopting these constraints, our method shows a remarkable improvement across different adaptation tasks using two benchmark datasets.

Both target-specific and domain-invariant features can facilitate Open Set Domain Adaptation (OSDA). To exploit these features, we propose a Knowledge Exchange (KnowEx) model which jointly trains two complementary constituent networks: (1) a Domain-Adversarial Network (DAdvNet) learning the domain-invariant representation, through which the supervision in source domain can be exploited to infer the class information of unlabeled target data; (2) a Private Network (PrivNet) exclusive for target domain, which is beneficial for discriminating between instances from known and unknown classes. The two constituent networks exchange training experience in the learning process. Toward this end, we exploit an adversarial perturbation process against DAdvNet to regularize PrivNet. This enhances the complementarity between the two networks. At the same time, we incorporate an adaptation layer into DAdvNet to address the unreliability of the PrivNet's experience. Therefore, DAdvNet and PrivNet are able to mutually reinforce each other during training. We have conducted thorough experiments on multiple standard benchmarks to verify the effectiveness and superiority of KnowEx in OSDA.

Class-conditional generative models have gained popularity due to their characteristics of learning disentangled representations. However, these models typically require labeled examples in training. In this paper, we explore the feasibility of training these models on completely unlabeled data, under the assumption that we have access to other labeled data. The labeled data share the same label space, while their domain is shifted. Our model, which we refer to as KTransGAN, incorporates a classifier to transfer knowledge from the labeled data and performs collaborative learning with the conditional generator. By adopting these measures, KTransGAN is able to approximate the conditional distribution of the unlabeled data and simultaneously introduces a new solution to the unsupervised domain adaptation problem. To mitigate the training difficulty of our generative adversarial networks-based model, variational encoding and feature matching are also considered. From the empirical results, KTransGAN exhibits outstanding performance on a number of synthetic datasets and multiple real-world benchmarks. The quality of the synthesized instances is far superior to the pure variational autoencoding model. For example, on the CIFAR-10 dataset, our model scores 35.3 in FID, while the other model scores 128.45. In addition, the synthesis quality is close to the case when the model is trained in a fully supervised setting over the same number of training iterations. Regarding the classification performance, for instance, our model surpasses the highest state-of-the-art results (89.19%) by a large margin and achieves a test accuracy of 95.31% on the unlabeled data SVHN, while MNIST represents the labeled data. These results highlight the effectiveness of our proposed framework.

We address the problem of unsupervised domain adaptation (UDA) which aims at adapting models trained on a labeled domain to a completely unlabeled domain. One way to achieve this goal is to learn a domain-invariant representation. However, this approach is subject to two challenges: samples from two domains are insufficient to guarantee domain-invariance at most part of the latent space, and neighboring samples from the target domain may not belong to the same class on the low-dimensional manifold. To mitigate these shortcomings, we propose two strategies. First, we incorporate a domain mixup strategy in domain adversarial learning model by linearly interpolating between the source and target domain samples. This allows the latent space to be continuous and yields an improvement of the domain matching. Second, the domain discriminator is regularized via judging the relative difference between both domains for the input mixup features, which speeds up the domain matching. Experiment results show that our proposed model achieves a superior performance on different tasks under various domain shifts and data complexity.

We propose a GAN-based scene-specific instance synthesis and classification model for semi-supervised pedestrian detection. Instead of collecting unreliable detections from unlabeled data, we adopt a class-conditional GAN for synthesizing pedestrian instances to alleviate the problem of insufficient labeled data. With the help of a base detector, we integrate pedestrian instance synthesis and detection by including a post-refinement classifier (PRC) into a minimax game. A generator and the PRC can mutually reinforce each other by synthesizing high-fidelity pedestrian instances and providing more accurate categorical information. Both of them compete with a class-conditional discriminator and a class-specific discriminator, such that the four fundamental networks in our model can be jointly trained. In our experiments, we validate that the proposed model significantly improves the performance of the base detector and achieves state-of-the-art results on multiple benchmarks. As shown in Figure 1, the result indicates the possibility of using inexpensively synthesized instances for improving semi-supervised detection models.