Computer Vision News - July 2019

When achieving minimum of the objective z*, the final image is then taken to be v*=G(z*). Since GAN is trained to produce natural looking images, the result of these optimization will generate more plausible results and improve the appearance of the previous optimization. Results In the field of deep image reconstruction, the results are the most important aspect of the model. While there are some quantitative measures for the results, we will let you examine the visual results produced from the method described above. We show here results from the paper on three different tasks: Reconstruction of shapes and letters, reconstruction of natural images and reconstruction of images from imagination. We start from the more straightforward task, reconstruction of shape and letters. You can see the results in the figure below: As you can see, these reconstructions are relatively clear: you can see the edges of the shapes and read the word NEURON from the reconstructed images. These results are the first of their kind which enable to understand the content of the images. Note that the images above are coming from a relatively simple distribution, hence nice results are expected. The next set of results are the natural images reconstruction. Note that this is much harder task since the images have much more complex distribution. It is analogous to a classification network that achieves great results on Mnist and 11 Research Computer Vision News … these reconstructions are relatively clear! ∗ = { 1 2 ෍ ቀΦ ሻ( − ൯ 2 } Deep Image Reconstruction from …