This paper is about Convolutional Neural Networks for Computer Vision. It was the first break-through in the ImageNet classification challenge (LSVRC-2010, 1000 classes).

ReLU was a key aspect which was not so often used before. The paper also used Dropout in the last two layers.

## Training details

* Momentum of 0.9
* Learning rate of $\varepsilon$ (initialized at 0.01)
* Weight decay of $0.0005 \cdot \varepsilon$.
* Batch size of 128
* The training took 5 to 6 days on two NVIDIA GTX 580 3GB GPUs.

## See also

* [Stanford presentation](http://vision.stanford.edu/teaching/cs231b_spring1415/slides/alexnet_tugce_kyunghee.pdf)

This paper solves two tasks: Image Captioning and VQA.
The main idea is to use Faster R-CNN to embed images (kx2048 from k bounding boxes) instead of ResNet (14x14x2048) and apply attention over k vectors.

For **VQA**, this is basically (Faster R-CNN + ShowAttendAskAnswer). SAAA(ShowAskAttendAnswer) calculates a 2D attention map from the concatenation of a text vector (2048-dim from LSTM) and image tensor (2048x14x14 from ResNet). This image feature can be thought as a collection of 2048-dim feature vectors. This paper uses Faster R-CNN to get k bounding boxes. Each bounding box is a 2048-dim vector so we have kx2048, which is fed to SAAA.


**SAAA**:
https://i.imgur.com/2FnPXi0.png

**This paper (VQA)**:
https://i.imgur.com/xib77Iy.png

For **Image Captioning**, it uses 2-layer LSTM. The first layer gets the average of k 2048-dim vectors. The output is used to calculate the attention weights over k vectors. The second layer gets the weight-averaged 2048-dim vector and the output of the first layer.

https://i.imgur.com/GeXaC30.png

Exploring an environment with non-linearities in a continuous action space can be optimized by regulating the agent curiosity with an homeostatic drive. This means that a heterostatic drive to move away from habitual states is blended with a homeostatic motivation to encourage actions that lead to states where the agent is familiar with a state-action pair. 

This approach  improves upon forward models and ICM Pathak et al 17 with an enhanced information gain that basically consists of the following: while the reward in \cite{Pathak17} is formulated as the forward model prediction error, the extended forward model loss in this paper is extended by substracting from the forward model prediction error the error knowing not only $s_t$ and $a_t$, but also $a_{t+1}$.

Curiosity-driven reinforcement learning shows that an additional homeostatic drive enhances the information gain of a classical curious/heterostatic agent. 

Implementation: They take advantage of a new Bellman-like equation of information gain and simplify the computation of the local rewards. It could help by prioritizing the exploration of the state-action space according to how hard is to learn each region. 

Background: The concept of homeostatic regulation in social robots was first proposed in Breazeal et al. 04. They extend existing approaches by compensating the heterostacity drive encouraged by the curiosity reward with an additional homeostatic drive.  1) The first component implements the heterostatic drive (same as referred to in Pathak et al 17). In other words, this one refers to the tendency to push away our agent from its habitual state; 2) Homeostatic motivation: the second component is our novel contribution. It encourages taking actions $a_t$ that lead to future states $s_{t+1}$ where the corresponding future action $a_{t+1}$ gives us additional information about $s_{t+1}$. This situation happens when the agent is "familiar" with the state-action pair: $\{s_{t+1}, a_{t+1}\}$.

The article misses exact comparison with Pathak et al regarding a joint task. In this paper the tasks consists of a 3 room navigation map is used to measure exploration.

Imagine you make a neural network mapping a scalar to a scalar. After you initialise this network in the traditional way, randomly with some given variance, you could take the gradient of the input with respect to the output for all reasonable values (between about - and 3 because networks typically assume standardised inputs). As the value increases, different rectified linear units in the network will randomly switch on, drawing a random walk in the gradients; another name for which is brown noise.

![](http://i.imgur.com/KMzfzMZ.png)

However, do the same thing for deep networks, and any traditional initialisation you choose, and you'll see the random walk start to look like white noise. One intuition given in the paper is that as different rectifiers in the network switch off and on the input is taking a number of different paths though the network. The number of possible paths grows exponentially with the depth of the network, so as the input varies, the gradients become increasingly chaotic. **The explanations and derivations given in the paper are much better reasoned and thorough, please read those if you are interested**.

Why should we care about this? Because the authors take the recent nonlinearity [CReLU][] (output is concatenation of `relu(x)` and `relu(-x)`) and develop an initialisation that will avoid problems with gradient shattering. The initialisation is just to take your standard initialised weight matrix $\mathbf{W}$ and set the right half to be the negative of the left half ($\mathbf{W}_{\text{left}}$). As long as the input to the layer is also concatenated, the left half will be multiplied by `relu(x)` and the right by `relu(-x)`. Then:

$$
\mathbf{W}.\text{CReLU}(\mathbf{x}) = \begin{cases} \mathbf{W}_{\text{left}}\mathbf{x} & \text{ if } x > 0 \\ \mathbf{W}_{\text{left}}\mathbf{x} & \text{ if } x \leq 0\end{cases}
$$

Doing this allows them to train deep networks without skip connections, and they show results on CIFAR-10 with depths of up to 200 exceeding (slightly) a similar resnet.

[crelu]: https://arxiv.org/abs/1603.05201

#### Introduction

* The paper demonstrates how simple CNNs, built on top of word embeddings, can be used for sentence classification tasks.
* [Link to the paper](https://arxiv.org/abs/1408.5882)
* [Implementation](https://github.com/shagunsodhani/CNN-Sentence-Classifier)

#### Architecture

* Pad input sentences so that they are of the same length.
* Map words in the padded sentence using word embeddings (which may be either initialized as zero vectors or initialized as word2vec embeddings) to obtain a matrix corresponding to the sentence.
* Apply convolution layer with multiple filter widths and feature maps.
* Apply max-over-time pooling operation over the feature map.
* Concatenate the pooling results from different layers and feed to a fully-connected layer with softmax activation.
* Softmax outputs probabilistic distribution over the labels.
* Use dropout for regularisation.

#### Hyperparameters

* RELU activation for convolution layers
* Filter window of 3, 4, 5 with 100 feature maps each.
* Dropout - 0.5
* Gradient clipping at 3
* Batch size - 50
* Adadelta update rule.

#### Variants

* CNN-rand
    * Randomly initialized word vectors.
* CNN-static
    * Uses pre-trained vectors from word2vec and does not update the word vectors.
* CNN-non-static
    * Same as CNN-static but updates word vectors during training.
* CNN-multichannel
    * Uses two set of word vectors (channels).
    * One set is updated and other is not updated.

#### Datasets

* Sentiment analysis datasets for Movie Reviews, Customer Reviews etc.
* Classification data for questions.
* Maximum number of classes for any dataset - 6

#### Strengths

* Good results on benchmarks despite being a simple architecture.
* Word vectors obtained by non-static channel have more meaningful representation. 

#### Weakness

* Small data with few labels.
* Results are not very detailed or exhaustive.