Existing long-tailed recognition methods, aiming totrain class-balanced models from long-tailed data, generally assume the models would be evaluated on the uniform test class distribution.

We theoretically show that our self-supervised strategy has a provable ability to simulate test-agnostic class distributions.

When a highly coherent light beam, such as that emitted by radars, is diffusely reflected on a surface with a rough structure (e.g., a piece of paper, white paint or a metallic surface), it produces a random granular effect known as the'speckle' pattern. This effect results in strong fluctuations that can reduce the quality and interpretability of images collected by synthetic aperture radar (SAR) techniques. SAR is an imaging method that can produce fine-resolution 2D or 3D images using a resolution-limited radar system. It is often employed to collect images of landscapes or object reconstructions, which can be used to create millimeter-to-centimeter scale models of the surface of Earth or other planets. To improve the quality and reliability of SAR data, researchers worldwide have been trying to develop techniques based on deep neural networks that could reduce the speckle effect. While some of these techniques have achieved promising results, their performance is still not optimal.

Existing works for aspect-based sentiment analysis (ABSA) have adopted a unified approach, which allows the interactive relations among subtasks. However, we observe that these methods tend to predict polarities based on the literal meaning of aspect and opinion terms and mainly consider relations implicitly among subtasks at the word level. In addition, identifying multiple aspect-opinion pairs with their polarities is much more challenging. Therefore, a comprehensive understanding of contextual information w.r.t. the aspect and opinion are further required in ABSA. In this paper, we propose Deep Contextualized Relation-Aware Network (DCRAN), which allows interactive relations among subtasks with deep contextual information based on two modules (i.e., Aspect and Opinion Propagation and Explicit Self-Supervised Strategies). Especially, we design novel self-supervised strategies for ABSA, which have strengths in dealing with multiple aspects. Experimental results show that DCRAN significantly outperforms previous state-of-the-art methods by large margins on three widely used benchmarks.