The paper introduces a new approach of how classical policy search can be combined and improved with trajectory optimization methods serving as exploration strategy. An optimization criteria with the goal of finding optimal policy parameters is decomposed with a variational approach. The variational distribution is approximated as Gaussian distribution which allows a solution with the iterative LQR algorithm. The overall algorithm uses expectation maximization to iterate between minimizing the KL divergence of the variational decomposition and maximizing the lower bound with respect to the policy parameters. 

## General stuff about face recognition

Face recognition has 4 main tasks:

* **Face detection**: Given an image, draw a rectangle around every face
* **Face alignment**: Transform a face to be in a canonical pose
* **Face representation**: Find a representation of a face which is suitable for follow-up tasks (small size, computationally cheap to compare, invariant to irrelevant changes)
* **Face verification**: Images of two faces are given. Decide if it is the same person or not.

The face verification task is sometimes (more simply) a face classification task (given a face, decide which of a fixed set of people it is).

Datasets being used are:

* **LFW** (Labeled Faces in the Wild): 97.35% accuracy; 13 323 web photos of 5 749 celebrities
* **YTF** (YouTube Faces): 3425 YouTube videos of 1 595 subjects
* **SFC** (Social Face Classification): 4.4 million labeled faces from 4030 people, each 800 to 1200 faces
* **USF** (Human-ID database): 3D scans of faces

## Ideas in this paper

This paper deals with face alignment and face representation.

**Face Alignment**

They made an average face with the USF dataset. Then, for each new face, they apply the following procedure:

* Find 6 points in a face (2 eyes, 1 nose tip, 2 corners of the lip, 1 middle point of the bottom lip)
* Crop according to those
* Find 67 points in the face / apply them to a normalized 3D model of a face
* Transform (=align) face to a normalized position

**Representation**

Train a neural network on 152x152 images of faces to classify 4030 celebrities. Remove the softmax output layer and use the output of the second-last layer as the transformed representation.

The network is:

* C1 (convolution): 32 filters of size $11 \times 11 \times 3$ (RGB-channels) (returns $142\times 142$ "images")
* M2 (max pooling): $3 \times 3$, stride of 2  (returns $71\times 71$ "images")
* C3 (convolution): 16 filters of size $9 \times 9 \times 16$ (returns $63\times 63$ "images")
* L4 (locally connected): $16\times9\times9\times16$ (returns $55\times 55$ "images")
* L5 (locally connected): $16\times7\times7\times16$ (returns $25\times 25$ "images")
* L6 (locally connected): $16\times5\times5\times16$ (returns $21\times 21$ "images")
* F7 (fully connected): ReLU, 4096 units
* F8 (fully connected): softmax layer with 4030 output neurons

The training was done with:

* Stochastic Gradient Descent (SGD)
* Momentum of 0.9
* Performance scheduling (LR starting at 0.01, ending at 0.0001)
* Weight initialization: $w \sim \mathcal{N}(\mu=0, \sigma=0.01)$, $b = 0.5$
* ~15 epochs ($\approx$ 3 days) of training


## Evaluation results

* **Quality**:
  * 97.35% accuracy (or mean accuracy?) with an Ensemble of DNNs for LFW
  * 91.4% accuracy with a single network on YTF
* **Speed**: DeepFace runs in 0.33 seconds per image (I'm not sure which size). This includes image decoding, face detection and alignment, **the** feed forward network (why only one? wasn't this the best performing Ensemble?) and final classification output

## See also

* Andrew Ng: [C4W4L03 Siamese Network](https://www.youtube.com/watch?v=6jfw8MuKwpI)

This paper presents Swapout, a simple dropout method applied to Residual Networks (ResNets). In a ResNet, a layer $Y$ is computed from the previous layer $X$ as

$Y = X + F(X)$

where $F(X)$ is essentially the composition of a few convolutional layers. Swapout simply applies dropout separately on both terms of a layer's equation:

$Y = \Theta_1 \odot X + \Theta_2 \odot F(X)$

where $\Theta_1$ and $\Theta_2$ are independent dropout masks for each term. 

The paper shows that this form of dropout is at least as good or superior as other forms of dropout, including the recently proposed [stochastic depth dropout][1]. Much like in the stochastic depth paper, better performance is achieved by linearly increasing the dropout rate (from 0 to 0.5) from the first hidden layer to the last.

In addition to this observation, I also note the following empirical observations:

1. At test time, averaging the output layers of multiple dropout mask samples (referenced to as stochastic inference) is better than replacing the masks by their expectation (deterministic inference), the latter being the usual standard.
2. Comparable performance is achieved by making the ResNet wider (e.g. 4 times) and with fewer layers (e.g. 32) than the orignal ResNet work with thin but very deep (more than 1000 layers) ResNets. This would confirm a similar observation from [this paper][2].

Overall, these are useful observations to be aware of for anyone wanting to use ResNets in practice.

[1]: http://arxiv.org/abs/1603.09382v1
[2]: https://arxiv.org/abs/1605.07146

**Object detection** is the task of drawing one bounding box around each instance of the type of object one wants to detect. Typically, image classification is done before object detection. With neural networks, the usual procedure for object detection is to train a classification network, replace the last layer with a regression layer which essentially predicts pixel-wise if the object is there or not. An bounding box inference algorithm is added at last to make a consistent prediction (see [Deep Neural Networks for Object Detection](http://papers.nips.cc/paper/5207-deep-neural-networks-for-object-detection.pdf)).

The paper introduces RPNs (Region Proposal Networks). They are end-to-end trained to generate region proposals.They simoultaneously regress region bounds and bjectness scores at each location on a regular grid.

RPNs are one type of fully convolutional networks. They take an image of any size as input and output a set of rectangular object proposals, each with an objectness score.

## See also
* [R-CNN](http://www.shortscience.org/paper?bibtexKey=conf/iccv/Girshick15#joecohen)
* [Fast R-CNN](http://www.shortscience.org/paper?bibtexKey=conf/iccv/Girshick15#joecohen)
* [Faster R-CNN](http://www.shortscience.org/paper?bibtexKey=conf/nips/RenHGS15#martinthoma)
* [Mask R-CNN](http://www.shortscience.org/paper?bibtexKey=journals/corr/HeGDG17)

This paper presents an approach to initialize a neural network from the parameters of a smaller and previously trained neural network. This is effectively done by increasing the size (in width and/or depth) of the previously trained neural network, in such of a way that the function represented by the network doesn't change (i.e. the output of the larger neural network is still the same). The motivation here is that initializing larger neural networks in this way allows to accelerate their training, since at initialization the neural network will already be quite good.

In a nutshell, neural networks are made wider by adding several copies (selected randomly) of the same hidden units to the hidden layer, for each hidden layer. To ensure that the neural network output remains the same, each incoming connection weight must also be divided by the number of replicas that unit is connected to in the previous layer. If not training using dropout, it is also recommended to add some noise to this initialization, in order to break its initial symmetry (though this will actually break the property that the network's output is the same). As for making a deeper network, layers are added by initializing them to be the identity function. For ReLU units, this is achieved using an identity matrix as the connection weight matrix. For units based on sigmoid or tanh activations, unfortunately it isn't possible to add such identity layers.

In their experiments on ImageNet, the authors show that this initialization allows them to train larger networks faster than if trained from random initialization. More importantly, they were able to outperform their previous validation set ImageNet accuracy by initializing a very large network from their best Inception network.