ilfoki.blogg.se - Adversarial network radar

Here the standard segmentation metrics that are used in Cityscapes dataset such as per-pixel accuracy, per class IoU and mean class IoU. This label map can then be compared against the input ground truth labels using standard semantic segmentation metrics. We show that in such an environment, simple sub-band selection algorithms are unable to consistently attain high SINR. The FCN predicts a label map for a generated photo. The network consists of several independent radar nodes, which attempt to attain the highest possible SINR in each of many time steps. FCN Scores: For Cityscapes labels?photo dataset the authors FCN score.Participants were shown a sequence of pairs of images, one a real photo or map and one fake (generated by our algorithm or a baseline), and asked to click on the image they thought was real. AMT perceptual Studies : For the map?aerial photo task, the authors run “real vs fake” perceptual studies on Amazon Mechanical Turk (AMT) to assess the realism of our outputs.For this task the model transforms images from smartphone to DSLR quality images. For this the model takes images from two categories which are captured from smartphone camera (usually have deep Depth of Field due to low aperture ) to DSLR (which have lower depth of Field).

Photo enhancement : CycleGAN can also be used for photo enhancement.

However to improve this transformation., the authors also introduced an additional loss called Identity loss.

Photo Generation from Painting: CycleGAN can also be used to transform photo from paintings and vice-versa.

Therefore it can generate different styles such as : Van Gogh, Cezanne, Monet, and Ukiyo-e. Unlike other works on neural style transfer, CycleGAN learns to mimic the style of an entire collection of artworks, rather than transferring the style of a single selected piece of art.

Collection Style Transfer: The authors trained the model on landscape photographs downloaded from Flickr and WikiArt.

The Cost function we used is the sum of adversarial loss and cyclic consistent loss: The behavior induced by this loss function cause closely matching the real input (x) and F(G(x)) This loss function used in Cycle GAN to measure the error rate of inverse mapping G(x) -> F(G(x)). For this to happen the author proposed that process should be cycle-consistent. Thus adversarial mapping cannot guarantee the input x i to y i.

Cycle Consistency Loss: Given a random set of images adversarial network can map the set of input image to random permutation of images in the output domain which may induce the output distribution similar to target distribution.

ML | One Hot Encoding to treat Categorical data parameters.

ML | Label Encoding of datasets in Python.

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