While hand pose estimation is a critical component of most interactive extended reality and gesture recognition systems, contemporary approaches are not optimized for computational and memory efficiency. In this paper, we propose a tiny deep neural network of which partial layers are recursively exploited for refining its previous estimations. During its iterative refinements, we employ learned gating criteria to decide whether to exit from the weight-sharing loop, allowing per-sample adaptation in our model. Our network is trained to be aware of the uncertainty in its current predictions to efficiently gate at each iteration, estimating variances after each loop for its keypoint estimates. Additionally, we investigate the effectiveness of end-to-end and progressive training protocols for our recursive structure on maximizing the model capacity. With the proposed setting, our method consistently outperforms state-of-the-art 2D/3D hand pose estimation approaches in terms of both accuracy and efficiency for widely used benchmarks.

We tackle the blackbox issue of deep neural networks in the settings of reinforcement learning (RL) where neural agents learn towards maximizing reward gains in an uncontrollable way. Such learning approach is risky when the interacting environment includes an expanse of state space because it is then almost impossible to foresee all unwanted outcomes and penalize them with negative rewards beforehand. We propose Action-conditional $\beta$-VAE (AC-$\beta$-VAE) that allows succinct mappings of action-dependent factors in desirable dimensions of latent representations while disentangling environmental factors. Our proposed method tackles the blackbox issue by encouraging an RL policy network to learn interpretable latent features by distinguits influenshing ices from uncontrollable environmental factors, which closely resembles the way humans understand their scenes. Our experimental results show that the learned latent factors not only are interpretable, but also enable modeling the distribution of entire visited state-action space. We have experimented that this characteristic of the proposed structure can lead to ex post facto governance for desired behaviors of RL agents.

Recent advances in image-to-image translation have led to some ways to generate multiple domain images through a single network. However, there is still a limit in creating an image of a target domain without a dataset on it. We propose a method to expand the concept of `multi-domain' from data to the loss area, and to combine the characteristics of each domain to create an image. First, we introduce a sym-parameter and its learning method that can mix various losses and can synchronize them with input conditions. Then, we propose Sym-parameterized Generative Network (SGN) using it. Through experiments, we confirmed that SGN could mix the characteristics of various data and losses, and it is possible to translate images to any mixed-domain without ground truths, such as 30% Van Gogh and 20% Monet and 40% snowy.

We introduce the Genetic-Gated Networks (G2Ns), simple neural networks that combine a gate vector composed of binary genetic genes in the hidden layer(s) of networks. Our method can take both advantages of gradient-free optimization and gradient-based optimization methods, of which the former is effective for problems with multiple local minima, while the latter can quickly find local minima. In addition, multiple chromosomes can define different models, making it easy to construct multiple models and can be effectively applied to problems that require multiple models. We show that this G2N can be applied to typical reinforcement learning algorithms to achieve a large improvement in sample efficiency and performance.