This paper learns deep local patch descriptor (for replacing SIFT) by hard negative mining using current mini-batch. It outperforms SIFT and deep competitors on Oxford5K and Paris6K retrieval datasets.

This paper introduces triangle-GAN ($\triangle$-GAN) that aims at cross-domain joint distribution matching: The model is shown below

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

Having two domains of data $x$ and $y$, there are two generators:

1- $G_x(y)$ which takes $y$ and generates $\tilde{x}$

2- $G_y(x)$ which takes $x$ and generates $\tilde{y}$

There are two discriminators in the model:

1- $D_1 (x,y)$ a discriminator that distinguishes between $(x, y)$ and either of $(x, \tilde{y})$ or $(\tilde{x}, y)$.

2- $D_2 (x,y)$ a discriminator that distinguishes between $(x, \tilde{y})$ and $(\tilde{x}, y)$.

The second discriminator is ALI and can be used on un-paired sets of data.
The first discriminator is equivalent to a conditional discriminator where the true paired data $(x, y)$ is compared to either $(x, \tilde{y})$ or $(\tilde{x}, y)$, where one element in the pair is sampled. This discriminator needs paired $(x, y)$ data for training. 

This model can be used for semi-supervised settings, where a small set of paired data is provided. In this paper it is used for:

-  semi-supervised image classification, where a small subset of CIFAR10 is labelled. $x$ and $y$ are images and class labels here.

- image to image translation on edge2shoes dataset, where only a subset of dataset is paired.

- attribute conditional image generation where $x$ and $y$ domains are image and attributes. CelebA and COCO datasets are used here. In one experiment test-set images are projected to attributes and then given those attributes new images are generated:

On celebA:
https://i.imgur.com/EX5tDZ0.png

On COCO:
https://i.imgur.com/GRpvjGx.png

In another experiment some attributes are chosen (as samples shown below in the first row with different noise) and then another feature is added (using the same noise) to generate the samples in the second row:
 https://i.imgur.com/KeHL8Ye.png

The triangle gan demonstrates improved performance compared to triple gan in the experiments shown in the paper. It has been also compared with Disco gan (a model that can be trained on un-paired data) and shows improved performance when some percentage of paired data is provided. In an experiment they pair each MNIST digit with its transposed (as $x$, $y$ pairs). Disco-GAN cannot learn correct mapping between them, while triangle-GAN can learn correct mapping since it leverages paired data.

https://i.imgur.com/Vz9Zfhu.png

In general this model is a useful approach for semi-supervised cross-domain matching and can leverage un-paired data (using ALI) as well as paired data (using conditional discriminator). 

_Objective:_ Introduces the Convolutional Auto-Encoder, a hierarchical unsupervised feature extractor.

_Dataset:_ [MNIST](yann.lecun.com/exdb/mnist/) and [SVHN](ufldl.stanford.edu/housenumbers/).

#### Architecture:

Uses convolutions to generate an encoding of the image and then decodes it and do a pixel-wise comparison.  
Used to initializes CNN.

#### Results:

Old article, not really relevant nowadays. They don't speak about the deconvolution part.

The authors present an algorithm for investment (picking which stocks to buy).  This online algorithm chooses an investment in N stocks (a point on the N-dimensional simplex) every period.  It works by optimizing, every period, some approximation to the current lossdefining a portfolio loss minus the two-norm of the investment vector.  This algorithm can be easily implemented and the authors claim is has optimal regret with respect to the best CRP (constantly rebalanced portfolio) chosen in hindsight (I leave technical details of the algorithm to those who want to read the paper).  The authors have experiments indicating that the algorithms does better than buy-and-hold on randomly selected S&P stocks.  My personal experiments with this algorithm (using code from one of the authors) indicated that the regret bounds, though optimal, are far too weak to be economically meaningful (the terminal wealth of ONS may lag the best CRP by a factor exponential in $22 \cdot N \cdot \sqrt{T}$, where $N$ is the number of stocks and T the number of periods) and the algorithm performed roughly as well as buy-and-hold on monthly data.

This paper aims to do zero-shot action recognition which uses cluster-based representation.
Concretely, it uses REINFORCE algorithm which is a Reinforcement Learning algorithm to optimize the centroids and the reward signal is the classification scores.
https://i.imgur.com/gWyJLX0.png