Modern societies are characterized by a large degree of pluralism in social, political and cultural opinions. In addition, there is evidence that humans tend to form distinct subgroups (clusters), characterized by opinion consensus within the clusters and differences between them. So far, however, formal theories of social influence have difficulty explaining this coexistence of global diversity and opinion clustering. This paper identifies a missing ingredient that helps to fill this gap: the striving for uniqueness. Besides being influenced by their social environment, individuals also show a desire to hold a unique opinion. Thus, when too many other members of the population hold a similar opinion, individuals tend to adopt an opinion that distinguishes them from others. This notion is rooted in classical sociological theory and is supported by recent empirical research. Authors develop a computational model of opinion dynamics in human populations and demonstrate that the new model can explain opinion clustering. Authors conduct simulation experiments to study the conditions of clustering. Based on our results, we discuss preconditions for the persistence of pluralistic societies in a globalizing world.

_Objective:_ Transfer visual attribute (color, tone, texture, and style, etc) between two semantically-meaningful images such as a picture and a sketch.

## Inner workings:

### Image analogy

An image analogy A:A′::B:B′ is a relation where:

*   B′ relates to B in the same way as A′ relates to A
*   A and A′ are in pixel-wise correspondences
*   B and B′ are in pixel-wise correspondences

In this paper only a source image A and an example image B′ are given, and both A′ and B represent latent images to be estimated.

[![screen shot 2017-05-18 at 10 43 48 am](https://cloud.githubusercontent.com/assets/17261080/26193907/f080e212-3bb6-11e7-9441-7b255e4219f5.png)](https://cloud.githubusercontent.com/assets/17261080/26193907/f080e212-3bb6-11e7-9441-7b255e4219f5.png)

### Dense correspondence

In order to find dense correspondences between two images they use features from previously trained CNN (VGG-19) and retrieve all the ReLU layers.

The mapping is divided in two sub-mappings that are easier to compute, first a visual attribute transformation and then a space transformation.

[![screen shot 2017-05-18 at 11 04 58 am](https://cloud.githubusercontent.com/assets/17261080/26194835/03ccd94a-3bba-11e7-93ca-9420d4d96162.png)](https://cloud.githubusercontent.com/assets/17261080/26194835/03ccd94a-3bba-11e7-93ca-9420d4d96162.png)

## Architecture:

The algorithm proceeds as follow:

1.  Compute features at each layer for the input image using a pre-trained CNN and initialize feature maps of latent images with coarsest layer.
2.  For said layer compute a forward and reverse nearest-neighbor field (NNF, basically an offset field).
3.  Use this NNF with the feature of the input current layer to compute the features of the latent images.
4.  Upsample the NNF and use it as the initialization for the NNF of the next layer.

[![screen shot 2017-05-18 at 11 14 33 am](https://cloud.githubusercontent.com/assets/17261080/26195178/35277e0e-3bbb-11e7-82ce-037466314640.png)](https://cloud.githubusercontent.com/assets/17261080/26195178/35277e0e-3bbb-11e7-82ce-037466314640.png)

## Results:

Impressive quality on all type of visual transfer but veryyyyy slow! (~3min on GPUs for one image).

[![screen shot 2017-05-18 at 11 36 47 am](https://cloud.githubusercontent.com/assets/17261080/26196151/54ef423c-3bbe-11e7-9433-b29be5091fae.png)](https://cloud.githubusercontent.com/assets/17261080/26196151/54ef423c-3bbe-11e7-9433-b29be5091fae.png)

GANs for images have made impressive progress in recent years, reaching ever-higher levels of subjective realism. It’s also interesting to think about domains where the GAN architecture is less of a good fit. An example of one such domain is natural language. 

As opposed to images, which are made of continuous pixel values, sentences are fundamentally sequences of discrete values: that is, words. In a GAN, when the discriminator makes its assessment of the realness of the image, the gradient for that assessment can be backpropagated through to the pixel level. The discriminator can say “move that pixel just a bit, and this other pixel just a bit, and then I’ll find the image more realistic”. However, there is no smoothly flowing continuous space of words, and, even if you use continuous embeddings of words, it’s still the case that if you tried to apply a small change to a embedding vector, you almost certainly wouldn’t end up with another word, you’d just be somewhere in the middle of nowhere in word space. In short: the discrete nature of language sequences doesn’t allow for gradient flow to propagate backwards through to the generator. 

The authors of this paper propose a solution: instead of trying to treat their GAN as one big differentiable system, they framed the problem of “generate a sequence that will seem realistic to the discriminator” as a reinforcement learning problem? After all, this property - of your reward just being generated *somewhere* in the environment, not something analytic, not something you can backprop through -  is one of the key constraints of reinforcement learning. Here, the more real the discriminator finds your sequence, the higher the reward. One approach to RL, and the one this paper uses, is that of a policy network, where your parametrized network produces a distribution over actions. You can’t update your model to deterministically increase reward, but you can shift around probability in your policy such that your expected reward of following that policy is higher. 

This key kernel of an idea - GANs for language, but using a policy network framework to get around not having backprop-able loss/reward- gets you most of the way to understanding what these authors did, but it’s still useful to mechanically walk through specifics. 

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

At each step, the “state” is the existing words in the sequence, and the agent’s “action” the choosing of its next word
- The Discriminator can only be applied to completed sequences, since it's difficult to determine whether an incoherent half-sentence is realistic language. So, when the agent is trying to calculate the reward of an action at a state, it uses Monte Carlo Tree Search: randomly “rolling out” many possible futures by randomly sampling from the policy, and then taking the average Discriminator judgment of all those futures resulting from each action as being its expected reward
- The Generator is a LSTM that produces a softmax over words, which can be interpreted as a policy if it’s sampled from randomly 
- One of the nice benefits of this approach is that it can work well for cases where we don't have a hand-crafted quality assessment metric, the way we have BLEU score for translation 

### Read-Again
Two options:
* GRU: run a pass of regular GRU on the input text $x_1,\ldots,x_n$. Use its hidden states $h_1,\ldots,h_n$ to compute weights vector for every step $i$ :
$\alpha_i = \tanh \left( W_e h_i  + U_e h_n + V_e x_i\right)$ and then runs a second GRU pass on the same input text. In the second pass the weights $\alpha_i$, from the first pass, are multiplied with the internal $z_i$ GRU gatting (controlling if hidden state is directly copied) of the second pass.
* LSTM: concatenate the hidden states from the first pass with the input text
$\left[ x_i, h_i, h_n \right]$ and run a second pass on this new input.

In case of multiple sentences the above passes are done per sentence. In addition the $h^s_n$ of each sentence $s$ is concatenated with the $h^{s'}_n$ of the other sentences or with $\tanh \left( \sum_s V_s h_s  + v\right)$

### Decoder with copy mechanism
LSTM with hidden state $s_t$. Input is previously generated word $y_{t-1}$ and context computed with attention mechanism: $c_t = \sum_i^n \beta_{it} h_i$. Here $h_i$ are the hidden states of the 2nd pass of the encoder. The weights are $\beta_{it} = \text{softmax}  \left(  v_a^T \tanh \left( W_a s_{t-1}  + U_a h_i\right) \right)$

The decoder vocabulary $Y$ used is small. If $y_{t-1}$ does not appear in $Y$ but does appear in the input at $x_i$ then its embedding is replaced with $p_t = \tanh \left( W_c h_i + b_c\right)$ and <UNK> otherwise.

$p_t$ is also used to copy the input to the output (details not given)

### Experiments
abstractive summarization [DUC2003 and DUC2004 competitions](http://www-nlpir.nist.gov/projects/duc/data.html).

For all animals, the taste sense is crucial to detect and avoid ingesting toxic molecules. Many toxins are synthesized by plants as a defense mechanism against insect predation. One example of such a natural toxic molecule is L-canavanine, a nonprotein amino acid found in the seeds of many legumes. Whether and how insects are informed that some plants contain L-canavanine remains to be elucidated. In insects, the taste sense relies on gustatory receptors forming the gustatory receptor (Gr) family. Gr proteins display highly divergent sequences, suggesting that they could cover the entire range of tastants. However, one cannot exclude the possibility of evolutionarily independent taste receptors. Here, we show that L-canavanine is not only toxic, but is also a repellent for Drosophila. Using a pharmacogenetic approach, we find that flies sense food containing this poison by the DmX receptor. DmXR is an insect orphan G-protein-coupled receptor that has partially diverged in its ligand binding pocket from the metabotropic glutamate receptor family. Blockade of DmXR function with an antagonist lowers the repulsive effect of L-canavanine. In addition, disruption of the DmXR encoding gene, called mangetout (mtt), suppresses the L-canavanine repellent effect. To avoid the ingestion of L-canavanine, DmXR expression is required in bitter-sensitive gustatory receptor neurons, where it triggers the premature retraction of the proboscis, thus leading to the end of food searching. These findings show that the DmX receptor, which does not belong to the Gr family, fulfills a gustatory function necessary to avoid eating a natural toxin.