Active Learning for discriminative models has largely been studied with the focus on individual samples, with less emphasis on how classes are distributed or which classes are hard to deal with. In this work, we show that this is harmful. We propose a method based on the Bayes’ rule, that can naturally incorporate class imbalance into the Active Learning framework. We derive that three terms should be considered together when estimating the probability of a classifier making a mistake for a given sample; i) probability of mislabelling a class, ii) likelihood of the data given a predicted class, and iii) the prior probability on the abundance of a predicted class. Implementing these terms requires a generative model and an intractable likelihood estimation. Therefore, we train a Variational Auto Encoder (VAE) for this purpose. To further tie the VAE with the classifier and facilitate VAE training, we use the classifiers’ deep feature representations as input to the VAE. By considering all three probabilities, among them, especially the data imbalance, we can substantially improve the potential of existing methods under limited data budget. We show that our method can be applied to classification tasks on multiple different datasets – including one that is a real-world dataset with heavy data imbalance – significantly outperforming the state of the art.

We describe an unsupervised domain adaptation framework for images by a transform to an abstract intermediate domain and ensemble classifiers seeking a consensus. The intermediate domain can be thought as a latent domain where both the source and target domains can be transferred easily. The proposed framework aligns both domains to the intermediate domain, which greatly improves the adaptation performance when the source and target domains are notably dissimilar. In addition, we propose an ensemble model trained by confusing multiple classifiers and letting them make a consensus alternately to enhance the adaptation performance for ambiguous samples. To estimate the hidden intermediate domain and the unknown labels of the target domain simultaneously, we develop a training algorithm using a double-structured architecture. We validate the proposed framework in hard adaptation scenarios with real-world datasets from simple synthetic domains to complex real-world domains. The proposed algorithm outperforms the previous state-of-the-art algorithms on various environments.

When a new problem is given to be solved with high performance, deep learning needs large effort to tune the model hyperparameters including model architectures and training hyperparameters. Many previous works have tried to tune the model hyperparameters automatically, but the algorithms only target on searching either model architectures or training hyperparameters. However, simultaneous optimization of the model architectures and the training hyperparameters works slow and falls into bad local minimums because of the search space enlarged by the high correlation between the two model hyperparameters. In this paper, we propose a novel algorithm to efficiently find the best set of model architectures and training hyperparameters. To efficiently handle the large search space, the proposed algorithm selectively utilizes the given training samples, while limiting the search space by a novel ensemble sampling method. Also, the evaluation time is further reduced by a novel termination mechanism. The accelerated computation of the proposed algorithm is validated by using complex image datasets, which shows the state-of-the-art performance with the 70.9% reduction of computational time.

HONS (Hybrid Open Networking Stack) is a system which can service diverse types of sensor nodes as a single network. By defining open packet format of IEEE 802.15.4 standard, it can form low-power multi-hop network of diverse sensors. HONS system is composed of routers and wireless sensor nodes. Routers are motes with wired power and form the basic backbone of the multi-hop network. Wireless sensor nodes are motes powered by battery, and operate in a lower-power mode, and are attached to the multi-hop network formed by routers.