Hierarchical Reinforcement Learning with Adversarially Guided Subgoals

Hierarchical reinforcement learning (HRL) proposes to solve difficult tasks by performing decision-making and control at successively higher levels of temporal abstraction. However, off-policy HRL often suffers from the problem of non-stationary high-level policy since the low-level policy is constantly changing. In this paper, we propose a novel HRL approach for mitigating the non-stationarity by adversarially enforcing the high-level policy to generate subgoals compatible with the current instantiation of the low-level policy. In practice, the adversarial learning is implemented by training a simple discriminator network concurrently with the high-level policy which determines the compatibility level of subgoals. Experiments with state-of-the-art algorithms show that our approach improves both HRL learning efficiency and overall performance in various challenging continuous control tasks.

Adversarially Guided Subgoal Generation for Hierarchical Reinforcement Learning

Hierarchical reinforcement learning (HRL) proposes to solve difficult tasks by performing decision-making and control at successively higher levels of temporal abstraction. However, off-policy training in HRL often suffers from the problem of non-stationary high-level decision making since the low-level policy is constantly changing. In this paper, we propose a novel HRL approach for mitigating the non-stationarity by adversarially enforcing the high-level policy to generate subgoals compatible with the current instantiation of the low-level policy. In practice, the adversarial learning can be implemented by training a simple discriminator network concurrently with the high-level policy which determines the compatibility level of subgoals. Experiments with state-of-the-art algorithms show that our approach significantly improves learning efficiency and overall performance of HRL in various challenging continuous control tasks.

Spectral Pyramid Graph Attention Network for Hyperspectral Image Classification

Convolutional neural networks (CNN) have made significant advances in hyperspectral image (HSI) classification. However, standard convolutional kernel neglects the intrinsic connections between data points, resulting in poor region delineation and small spurious predictions. Furthermore, HSIs have a unique continuous data distribution along the high dimensional spectrum domain - much remains to be addressed in characterizing the spectral contexts considering the prohibitively high dimensionality and improving reasoning capability in light of the limited amount of labelled data. This paper presents a novel architecture which explicitly addresses these two issues. Specifically, we design an architecture to encode the multiple spectral contextual information in the form of spectral pyramid of multiple embedding spaces. In each spectral embedding space, we propose graph attention mechanism to explicitly perform interpretable reasoning in the spatial domain based on the connection in spectral feature space. Experiments on three HSI datasets demonstrate that the proposed architecture can significantly improve the classification accuracy compared with the existing methods.

Simultaneously Learning Architectures and Features of Deep Neural Networks

This paper presents a novel method which simultaneously learns the number of filters and network features repeatedly over multiple epochs. We propose a novel pruning loss to explicitly enforces the optimizer to focus on promising candidate filters while suppressing contributions of less relevant ones. In the meanwhile, we further propose to enforce the diversities between filters and this diversity-based regularization term improves the trade-off between model sizes and accuracies. It turns out the interplay between architecture and feature optimizations improves the final compressed models, and the proposed method is compared favorably to existing methods, in terms of both models sizes and accuracies for a wide range of applications including image classification, image compression and audio classification.

Semi-Supervised Domain Adaptation for Weakly Labeled Semantic Video Object Segmentation

Deep convolutional neural networks (CNNs) have been immensely successful in many high-level computer vision tasks given large labeled datasets. However, for video semantic object segmentation, a domain where labels are scarce, effectively exploiting the representation power of CNN with limited training data remains a challenge. Simply borrowing the existing pretrained CNN image recognition model for video segmentation task can severely hurt performance. We propose a semi-supervised approach to adapting CNN image recognition model trained from labeled image data to the target domain exploiting both semantic evidence learned from CNN, and the intrinsic structures of video data. By explicitly modeling and compensating for the domain shift from the source domain to the target domain, this proposed approach underpins a robust semantic object segmentation method against the changes in appearance, shape and occlusion in natural videos. We present extensive experiments on challenging datasets that demonstrate the superior performance of our approach compared with the state-of-the-art methods.