This combines the ideas of recurrent attention to perform object detection in an image \cite{1406.6247} for multiple objects \cite{1412.7755} with semantic segmentation \cite{1505.04366}. 

Segmenting subregions is to avoid a global resolution bias (the object would take up the majority of pixels) and to allow multiple scales of objects to be segmented. 

Here is a video that demos the method described in the paper:

https://youtu.be/BMVDhTjEfBU

This work expands on prior techniques for designing models that can both be stored using fewer parameters, and also execute using fewer operations and less memory, both of which are key desiderata for having trained machine learning models be usable on phones and other personal devices. 

The main contribution of the original MobileNets paper was to introduce the idea of using "factored" decompositions of Depthwise and Pointwise convolutions, which separate the procedures of "pull information from a spatial range" and "mix information across channels" into two distinct steps. In this paper, they continue to use this basic Depthwise infrastructure, but also add a new design element: the inverted-residual linear bottleneck. 

The reasoning behind this new layer type comes from the observation that, often, the set of relevant points in a high-dimensional space (such as the 'per-pixel' activations inside a conv net) actually lives on a lower-dimensional manifold. So, theoretically, and naively, one could just try to use lower dimensional internal representations to map the dimensionality of that assumed manifold. However, the authors argue that ReLU non-linearities kill information (because of the region where all inputs are mapped to zero), and so having layers contain only the number of dimensions needed for the manifold would mean that you end up with too-few dimensions after the ReLU information loss. However, you need to have non-linearities somewhere in the network in order to be able to learn complex, non-linear functions. 

So, the authors suggest a method to mostly use smaller-dimensional representations internally, but still maintain ReLus and the network's needed complexity. 

https://i.imgur.com/pN4d9Wi.png

- A lower-dimensional output is "projected up" into a higher dimensional output
- A ReLu is applied on this higher-dimensional layer
- That layer is then projected down into a smaller-dimensional layer, which uses a linear activation to avoid information loss
- A residual connection between the lower-dimensional output at the beginning and end of the expansion

This way, we still maintain the network's non-linearity, but also replace some of the network's higher-dimensional layers with lower-dimensional linear ones

This is a nice little empirical paper that does some investigation into which features get learned during the course of neural network training. To look at this, it uses a notion of "decodability", defined as the accuracy to which you can train a linear model to predict a given conceptual feature on top of the activations/learned features at a particular layer. This idea captures the amount of information about a conceptual feature that can be extracted from a given set of activations. 

They work with two synthetic datasets. 

1. Trifeature: Generated images with a color, shape, and texture, which can be engineered to be either entirely uncorrelated or correlated with each other to varying degrees. 
2. Navon: Generated images that are letters on the level of shape, and are also composed of letters on the level of texture 

The first thing the authors investigate is: to what extent are the different properties of these images decodable from their representations, and how does that change during training? In general, decodability is highest in lower layers, and lowest in higher layers, which makes sense from the perspective of the Information Processing Inequality, since all the information is present in the pixels, and can only be lost in the course of training, not gained. They find that decodability of color is high, even in the later layers untrained networks, and that the decodability of texture and shape, while much less high, is still above chance. When the network is trained to predict one of the three features attached to an image, you see the decodability of that feature go up (as expected), but you also see the decodability of the other features go down, suggesting that training doesn't just involve amplifying predictive features, but also suppressing unpredictive ones. This effect is strongest in the Trifeature case when training for shape or color; when training for texture, the dampening effect on color is strong, but on shape is less pronounced. 

https://i.imgur.com/o45KHOM.png

The authors also performed some experiments on cases where features are engineered to be correlated to various degrees, to see which of the predictive features the network will represent more strongly. In the case where two features are perfectly correlated (and thus both perfectly predict the label), the network will focus decoding power on whichever feature had highest decodability in the untrained network, and, interestingly, will reduce decodability of the other feature (not just have it be lower than the chosen feature, but decrease it in the course of training), even though it is equally as predictive. 
https://i.imgur.com/NFx0h8b.png

Similarly, the network will choose the "easy" feature (the one more easily decodable at the beginning of training) even if there's another feature that is slightly *more* predictive available. This seems quite consistent with the results of another recent paper, Shah et al, on the Pitfalls of Simplicity Bias in neural networks. The overall message of both of these experiments is that networks generally 'put all their eggs in one basket,' so to speak, rather than splitting representational power across multiple features. 

There were a few other experiments in the paper, and I'd recommend reading it in full - it's quite well written - but I think those convey most of the key insights for me.

This paper presents a combination of the inception architecture
with residual networks. This is done by adding a shortcut connection
to each inception module. This can alternatively be seen as a resnet where
the 2 conv layers are replaced by a (slightly modified) inception module.
The paper (claims to) provide results against the hypothesis that adding residual
connections improves training, rather increasing the model size is what makes the difference.

Problem
---------
Video prediction with human objects


Contribution
--------------
Instead of the common approach of predicting directly in pixel-space, use explicit knowledge of human motion space to predict the future of the video.

Approach
--------------
1. VAE to model the possible future movements of humans in the pose space
2. Conditional GAN - use pose information for to predict video in pixel space.



https://image.ibb.co/b1omVF/The_pose_knows.png