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* DRAW = deep recurrent attentive writer * DRAW is a recurrent autoencoder for (primarily) images that uses attention mechanisms. * Like all autoencoders it has an encoder, a latent layer `Z` in the "middle" and a decoder. * Due to the recurrence, there are actually multiple autoencoders, one for each timestep (the number of timesteps is fixed). * DRAW has attention mechanisms which allow the model to decide where to look at in the input image ("glimpses") and where to write/draw to in the output image. * If the attention mechanisms are skipped, the model becomes a simple recurrent autoencoder. * By training the full autoencoder on a dataset and then only using the decoder, one can generate new images that look similar to the dataset images. ![DRAW Architecture](https://raw.githubusercontent.com/aleju/papers/master/neuralnets/images/DRAW_A_Recurrent_Neural_Network_for_Image_Generation__architecture.png?raw=true "DRAW Architecture") *Basic recurrent architecture of DRAW.* ### How * General architecture * The encoderdecoderpair follows the design of variational autoencoders. * The latent layer follows an ndimensional gaussian distribution. The hyperparameters of that distribution (means, standard deviations) are derived from the output of the encoder using a linear transformation. * Using a gaussian distribution enables the use of the reparameterization trick, which can be useful for backpropagation. * The decoder receives a sample drawn from that gaussian distribution. * While the encoder reads from the input image, the decoder writes to an image canvas (where "write" is an addition, not a replacement of the old values). * The model works in a fixed number of timesteps. At each timestep the encoder performs a read operation and the decoder a write operation. * Both the encoder and the decoder receive the previous output of the encoder. * Loss functions * The loss function of the latent layer is the KLdivergence between that layer's gaussian distribution and a prior, summed over the timesteps. * The loss function of the decoder is the negative log likelihood of the image given the final canvas content under a bernoulli distribution. * The total loss, which is optimized, is the expectation of the sum of both losses (latent layer loss, decoder loss). * Attention * The selective read attention works on image patches of varying sizes. The result size is always NxN. * The mechanism has the following parameters: * `gx`: xaxis coordinate of the center of the patch * `gy`: yaxis coordinate of the center of the patch * `delta`: Strides. The higher the strides value, the larger the read image patch. * `sigma`: Standard deviation. The higher the sigma value, the more blurry the extracted patch will be. * `gamma`: IntensityMultiplier. Will be used on the result. * All of these parameters are generated using a linear transformation applied to the decoder's output. * The mechanism places a grid of NxN gaussians on the image. The grid is centered at `(gx, gy)`. The gaussians are `delta` pixels apart from each other and have a standard deviation of `sigma`. * Each gaussian is applied to the image, the center pixel is read and added to the result. ![DRAW Attention](https://raw.githubusercontent.com/aleju/papers/master/neuralnets/images/DRAW_A_Recurrent_Neural_Network_for_Image_Generation__attention.png?raw=true "DRAW Attention") *The basic attention mechanism. (gx, gy) is the read patch center. delta is the strides. On the right: Patches with different sizes/strides and standard deviations/blurriness.* ### Results * Realistic looking generated images for MNIST and SVHN. * Structurally OK, but overall blurry images for CIFAR10. * Results with attention are usually significantly better than without attention. * Image generation without attention starts with a blurry image and progressively sharpens it. ![DRAW SVHN Results](https://raw.githubusercontent.com/aleju/papers/master/neuralnets/images/DRAW_A_Recurrent_Neural_Network_for_Image_Generation__svhn.png?raw=true "DRAW SVHN Results") *Using DRAW with attention to generate new SVHN images.*  ### Rough chapterwise notes * 1. Introduction * The natural way to draw an image is in a step by step way (add some lines, then add some more, etc.). * Most generative neural networks however create the image in one step. * That removes the possibility of iterative selfcorrection, is hard to scale to large images and makes the image generation process dependent on a single latent distribution (input parameters). * The DRAW architecture generates images in multiple steps, allowing refinements/corrections. * DRAW is based on varational autoencoders: An encoder compresses images to codes and a decoder generates images from codes. * The loss function is a variational upper bound on the loglikelihood of the data. * DRAW uses recurrance to generate images step by step. * The recurrance is combined with attention via partial glimpses/foveations (i.e. the model sees only a small part of the image). * Attention is implemented in a differentiable way in DRAW. * 2. The DRAW Network * The DRAW architecture is based on variational autoencoders: * Encoder: Compresses an image to latent codes, which represent the information contained in the image. * Decoder: Transforms the codes from the encoder to images (i.e. defines a distribution over images which is conditioned on the distribution of codes). * Differences to variational autoencoders: * Encoder and decoder are both recurrent neural networks. * The encoder receives the previous output of the decoder. * The decoder writes several times to the image array (instead of only once). * The encoder has an attention mechanism. It can make a decision about the read location in the input image. * The decoder has an attention mechanism. It can make a decision about the write location in the output image. * 2.1 Network architecture * They use LSTMs for the encoder and decoder. * The encoder generates a vector. * The decoder generates a vector. * The encoder receives at each time step the image and the output of the previous decoding step. * The hidden layer in between encoder and decoder is a distribution Q(Ztht^enc), which is a diagonal gaussian. * The mean and standard deviation of that gaussian is derived from the encoder's output vector with a linear transformation. * Using a gaussian instead of a bernoulli distribution enables the use of the reparameterization trick. That trick makes it straightforward to backpropagate "low variance stochastic gradients of the loss function through the latent distribution". * The decoder writes to an image canvas. At every timestep the vector generated by the decoder is added to that canvas. * 2.2 Loss function * The main loss function is the negative log probability: `log D(xct)`, where `x` is the input image and `ct` is the final output image of the autoencoder. `D` is a bernoulli distribution if the image is binary (only 0s and 1s). * The model also uses a latent loss for the latent layer (between encoder and decoder). That is typical for VAEs. The loss is the KLDivergence between Q(Ztht_enc) (`Zt` = latent layer, `ht_enc` = result of encoder) and a prior `P(Zt)`. * The full loss function is the expection value of both losses added up. * 2.3 Stochastic Data Generation * To generate images, samples can be picked from the latent layer based on a prior. These samples are then fed into the decoder. That is repeated for several timesteps until the image is finished. * 3. Read and Write Operations * 3.1 Reading and writing without attention * Without attention, DRAW simply reads in the whole image and modifies the whole output image canvas at every timestep. * 3.2 Selective attention model * The model can decide which parts of the image to read, i.e. where to look at. These looks are called glimpses. * Each glimpse is defined by its center (x, y), its stride (zoom level), its gaussian variance (the higher the variance, the more blurry is the result) and a scalar multiplier (that scales the intensity of the glimpse result). * These parameters are calculated based on the decoder output using a linear transformation. * For an NxN patch/glimpse `N*N` gaussians are created and applied to the image. The center pixel of each gaussian is then used as the respective output pixel of the glimpse. * 3.3 Reading and writing with attention * Mostly the same technique from (3.2) is applied to both reading and writing. * The glimpse parameters are generated from the decoder output in both cases. The parameters can be different (i.e. read and write at different positions). * For RGB the same glimpses are applied to each channel. * 4. Experimental results * They train on binary MNIST, cluttered MNIST, SVHN and CIFAR10. * They then classfiy the images (cluttered MNIST) or generate new images (other datasets). * They say that these generated images are unique (to which degree?) and that they look realistic for MNIST and SVHN. * Results on CIFAR10 are blurry. * They use binary crossentropy as the loss function for binary MNIST. * They use crossentropy as the loss function for SVHN and CIFAR10 (color). * They used Adam as their optimizer. * 4.1 Cluttered MNIST classification * They classify images of cluttered MNIST. To do that, they use an LSTM that performs N readglimpses and then classifies via a softmax layer. * Their model's error rate is significantly below a previous nondifferentiable attention based model. * Performing more glimpses seems to decrease the error rate further. * 4.2 MNIST generation * They generate binary MNIST images using only the decoder. * DRAW without attention seems to perform similarly to previous models. * DRAW with attention seems to perform significantly better than previous models. * DRAW without attention progressively sharpens images. * DRAW with attention draws lines by tracing them. * 4.3 MNIST generation with two digits * They created a dataset of 60x60 images, each of them containing two random 28x28 MNIST images. * They then generated new images using only the decoder. * DRAW learned to do that. * Using attention, the model usually first drew one digit then the other. * 4.4 Street view house number generation * They generate SVHN images using only the decoder. * Results look quite realistic. * 4.5 Generating CIFAR images * They generate CIFAR10 images using only the decoder. * Results follow roughly the structure of CIFARimages, but look blurry.
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