individual feature slices, we can design whether different feature slices share the same local index function. Such a weight sharing strategy can be applied at a modelwise level or at a stagewise level, which leads to the following designs of O2O DINs:

1.  *Modelwise O2O DINs*: the model only has a unique index function that is shared by all feature slices, even in different downsampling and upsampling stages. This is the most light-weight design;

2.  *Shared Stagewise O2O DINs*: the index function is also shared by feature slices, but every stage has stage-specific IndexNet. This design is also light-weight;

3.  *Unshared Stagewise O2O DINs*: even in the same stage, different feature slices have distinct index functions.

Similar to HINs, DINs can also be designed to have linear/nonlinear modeling ability. Fig. \[fig:depthwise\_networks\] shows an example when $k=2$. Note that, in contrast to HINs, DINs follow a multi-column architecture. Each column is responsible for predicting indices specific to a certain spatial location of all local regions. We implement DINs with group convolutions.

*Linear O2O DINs.* According to Fig. \[fig:depthwise\_networks\], the feature map first goes through four parallel convolutional layers with the same kernel size. Modelwise O2O DINs and Shared Stagewise O2O DINs 