The fundamental problem of Zero-Shot Learning (ZSL) is that the one-hot label space is discrete, which leads to a complete loss of the relationships between seen and unseen classes. Conventional approaches rely on using semantic auxiliary information, e.g. attributes, to re-encode each class so as to preserve the inter-class associations. However, existing learning algorithms only focus on unifying visual and semantic spaces without jointly considering the label space. More importantly, because the final classification is conducted in the label space through a compatibility function, the gap between attribute and label spaces leads to significant performance degradation. Therefore, this paper proposes a novel pathway that uses the label space to jointly reconcile visual and semantic spaces directly, which is named Attributing Label Space (ALS). In the training phase, one-hot labels of seen classes are directly used as prototypes in a common space, where both images and attributes are mapped. Since mappings can be optimized independently, the computational complexity is extremely low. In addition, the correlation between semantic attributes has less influence on visual embedding training because features are mapped into labels instead of attributes. In the testing phase, the discrete condition of label space is removed, and priori one-hot labels are used to denote seen classes and further compose labels of unseen classes. Therefore, the label space is very discriminative for the Generalized ZSL (GZSL), which is more reasonable and challenging for real-world applications. Extensive experiments on five benchmarks manifest improved performance over all of compared state-of-the-art methods.

The visual-semantic gap between the visual space (visual features) and semantic space (semantic attributes) is one of the main problems in the Generalized Zero-Shot Learning (GZSL) task. The essence of this problem is that the structure of manifolds in these two spaces is inconsistent, which makes it difficult to learn embeddings that unify visual features and semantic attributes for similarity measurement. In this work, we tackle this problem by proposing a multi-modal aggregated posterior aligning neural network based on Wasserstein Auto-encoders (WAE) which learns a shared latent space for visual features and semantic attributes. The key to our approach is that the aggregated posterior distribution of the latent representations encoded from visual features of each class is encouraged to be aligned with a Gaussian distribution predicted by the corresponding semantic attribute in the latent space. On one hand, requiring the latent manifolds of visual features and semantic attributes to be consistent preserves the inter-class association between seen and unseen classes. On the other hand, the aggregated posterior of each class is directly defined as a Gaussian in the latent space, which provides a reliable way to synthesize latent features for training classification models. Using the AWA1, AWA2, CUB, aPY, FLO, and SUN benchmark datasets, we extensively conducted comparative evaluations to demonstrate the advantages of our method over state-of-the-art approaches.

Many data sources, such as human poses, lie on low-dimensional manifolds that are smooth and bounded. Learning low-dimensional representations for such data is an important problem. One typical solution is to utilize encoder–decoder networks. However, due to the lack of effective regularization in latent space, the learned representations usually do not preserve the essential data relations. For example, adjacent video frames in a sequence may be encoded into very different zones across the latent space with holes in between. This is problematic for many tasks such as denoising because slightly perturbed data have the risk of being encoded into very different latent variables, leaving output unpredictable. To resolve this problem, we first propose a neighborhood geometric structure-preserving variational autoencoder (SP-VAE), which not only maximizes the evidence lower bound but also encourages latent variables to preserve their structures as in ambient space. Then, we learn a set of small surfaces to approximately bound the learned manifold to deal with holes in latent space. We extensively validate the properties of our approach by reconstruction, denoising, and random image generation experiments on a number of data sources, including synthetic Swiss roll, human pose sequences, and facial expression images. The experimental results show that our approach learns more smooth manifolds than the baselines. We also apply our approach to the tasks of human pose refinement and facial expression image interpolation where it gets better results than the baselines.

Offline reinforcement learning (RL) aims to learn the possible policy from a fixed dataset without real-time interactions with the environment. By avoiding the risky exploration of the robot, this approach is expected to significantly improve the robot’s learning efficiency and safety. However, due to errors in value estimation from out-of-distribution actions, most offline RL algorithms constrain or regularize the policy to the actions contained within the dataset. The cost of such methods is the introduction of new hyperparameters and additional complexity. In this article, we aim to adapt offline RL to robotic manipulation with minimal changes and to avoid evaluating out-of-distribution actions as much as possible. Therefore, we improve offline RL with in-sample advantage regularization (ISAR). To mitigate the impact of unseen actions, the ISAR learns the state-value function only with the dataset sample to regress the optimal action-value function. Our method calculates the advantage function of action–state pairs based on in-sample value estimation and adds a behavior cloning (BC) regularization term in the policy update. This improves sample efficiency with minimal changes, resulting in a simple and easy-to-implement method. The experiments of the D4RL robot benchmark and multigoal sparse rewards robotic tasks show that the ISAR achieves excellent performance comparable to current state-of-the-art algorithms without the need for complex parameter tuning and too much training time. In addition, we demonstrate the effectiveness of our method on a real-world robot platform.