Link to paper: [1506.02025] Spatial Transformer Networks
This paper introduces a neural networks module that can learn input-dependent spatial transformations and can be inserted into any neural network. It supports transformations like scaling, cropping, rotations, and non-rigid deformations. Main contributions:
- The spatial transformer network consists of the following:
- Localization network that regresses to the transformation parameters given the input.
- Grid generator that uses the transformation parameters to produce a grid to sample from the input.
- Sampler that produces the output feature map sampled from the input at the grid points.
- Differentiable sampling mechanism
- The sampling is written in a way such that sub-gradients can be defined with respect to grid coordinates.
- This enables gradients to be propagated through the grid generator and localization network, and for the network to jointly learn the spatial transformer along with rest of the network.
- A network can have multiple STNs
- at different points in the network, to model incremental transformations at different levels of abstraction.
- in parallel, to learn to focus on different regions of interest. For example, on the bird classification task, they show that one STN learns to be a head detector, while the other focuses on the central part of the body.
- Their attention (and by extension transformation) mechanism is differentiable as opposed to earlier works on non-differentiable attention mechanisms that used reinforcement learning (REINFORCE). It also supports a richer variety of transformations as opposed to earlier works on learning transformations, like DRAW.
- State-of-the-art classification performance on distorted MNIST, SVHN, CUB-200-2011.
This is a really nice way to generalize spatial transformations in a differentiable manner so the model can be trained end-to-end. Classification performance, and more importantly, qualitative results of the kind of transformations learnt on larger datasets (like ImageNet) should be evaluated.