GANs: An overview
Based on this paper.
Introduction
From the highest level, adversarial examples are basically the images that fool ConvNets.
Let’s think of two models, a generative model and a discriminative model.
The discriminative model has the task of determining whether a given image looks natural (an image from the dataset) or looks like it has been artificially created. The task of the generator is to create images so that the discriminator gets trained to produce the correct outputs.This can be thought of as a zero-sum or minimax two player game.
The analogy used in the paper reference is that the generative model is like “a team of counterfeiters, trying to produce and use fake currency” while the discriminative model is like “the police, trying to detect the counterfeit currency”. The generator is trying to fool the discriminator while the discriminator is trying to not get fooled by the generator. As the models train, both methods are improved until a point where the “counterfeits are indistinguishable from the genuine articles”.
Why is this concept important?
The discriminator now is aware of the “internal representation of the data” because it has been trained to understand the differences between real images from the dataset and artificially created ones. Thus, it can be used as a feature extractor that you can use in a CNN. Plus, you can just create really cool artificial images that look pretty natural.
Implementation: Generating Image Descriptions (2014)
What happens when you combine CNNs with RNNs (No, you don’t get R-CNNs, sorry )? But you do get one really amazing application.
The model described in the paper has training examples that have a sentence (or caption) associated with each image. This type of label is called a weak label, where segments of the sentence refer to (unknown) parts of the image. Using this training data, a deep neural network “infers the latent alignment between segments of the sentences and the region that they describe” (quote from the paper). Another neural net takes in the image as input and generates a description in text. Let’s take a separate look at the two components, alignment and generation.
Alignment Model
The goal of this part of the model is to be able to align the visual and textual data (the image and its sentence description). The model works by accepting an image and a sentence as input, where the output is a score for how well they match.
The first step is feeding the image into an R-CNN in order to detect the individual objects. This R-CNN was trained on ImageNet data. The top 19 (plus the original image) object regions are embedded to a 500 dimensional space. Now we have 20 different 500 dimensional vectors (represented by v in the paper) for each image. We have information about the image. Now, we want information about the sentence. We’re going to embed words into this same multimodal space. This is done by using a bidirectional recurrent neural network. From the highest level, this serves to illustrate information about the context of words in a given sentence. Since this information about the picture and the sentence are both in the same space, we can compute inner products to show a measure of similarity.
Generation Model
The alignment model has the main purpose of creating a dataset where you have a set of image regions (found by the RCNN) and corresponding text (thanks to the BRNN). Now, the generation model is going to learn from that dataset in order to generate descriptions given an image. The model takes in an image and feeds it through a CNN. The softmax layer is disregarded as the outputs of the fully connected layer become the inputs to another RNN. For those that aren’t as familiar with RNNs, their function is to basically form probability distributions on the different words in a sentence (RNNs also need to be trained just like CNNs do).
Why It’s Important
The interesting idea for me was that of using these seemingly different RNN and CNN models to create a very useful application that in a way combines the fields of Computer Vision and Natural Language Processing. It opens the door for new ideas in terms of how to make computers and models smarter when dealing with tasks that cross different fields.
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