**Channel-wise Soft Attention** is an attention mechanism in computer vision that assigns "soft" attention weights for each channel $c$. In soft channel-wise attention, the alignment weights are learned and placed "softly" over each channel. This would contrast with hard attention which would only selects one channel to attend to at a time.

Image: [Xu et al](http://proceedings.mlr.press/v37/xuc15.pdf)

**Poincaré Embeddings** learn hierarchical representations of symbolic data by embedding them into hyperbolic space -- or more precisely into an $n$-dimensional Poincaré ball. Due to the underlying hyperbolic geometry, this allows for learning of parsimonious representations of symbolic data by simultaneously capturing hierarchy and similarity. Embeddings are learnt based on
Riemannian optimization.

Poincaré Embeddings

Poincaré Embeddings for Learning Hierarchical Representations

Channel-wise Soft Attention

**DetNet** is a backbone convolutional neural network for object detection. Different from traditional pre-trained models for ImageNet classification, DetNet maintains the spatial resolution of the features even though extra stages are included. DetNet attempts to stay efficient by employing a low complexity dilated bottleneck structure.

Year	2017
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