Image Scale Augmentation is an augmentation technique where we randomly pick the short size of a image within a dimension range. One use case of this augmentation technique is in object detectiont asks.

**Hierarchical Style Disentanglement**, or **HiSD**,  aims to disentangle different styles in image-to-image translation models. It organizes the labels into a hierarchical structure, where independent tags, exclusive attributes, and disentangled styles are allocated from top to bottom. To make the styles identified to the tags and attributes, the authors carefully redesign the modules, phases, and objectives.

HiSD

Image-to-image Translation via Hierarchical Style Disentanglement

Image Scale Augmentation

**Multi-View Entity Representations**, or **MuVER**, is an approach for entity retrieval that constructs multi-view representations for entity descriptions and approximates the optimal view for mentions via a heuristic searching method. It matches a mention to the appropriate entity by comparing it with entity descriptions. Motivated by the fact that mentions with different contexts correspond to different parts in descriptions, multi-view representations are constructed for each description. Specifically, we segment a description into several sentences. We refer to each sentence as a view $v$, which contains partial information, to form a view set $\mathcal{V}$ of the entity $e$. The Figure illustrates an example that constructs a view set $\mathcal{V}$ for “Kobe Bryant”.

Year	2000
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