![]() Let’s look at a small sample of images from the facades dataset. ![]() We get a good sense of this by considering a more complicated example, that of the CMP facades dataset which pix2pix has a download link for. This makes pix2pix highly flexible and adaptable to a wide variety of situations, including ones where it is not easy to verbally or explicitly define the task we want to model. It makes no assumptions about the relationship and instead learns the objective during training, by comparing the defined inputs and outputs during training, and inferring the objective. The nice thing about pix2pix is that it is generic it does not require pre-defining the relationship between the two types of images. Deblurring or denoising images can be framed in this way, and indeed there had been a great deal of past research in learning various specific image-to-image translation tasks like those and others. Many important image processing tasks can be framed as image to image translation tasks of this sort. We should expect that it will be able to color new, previously unseen black & white images to around the same accuracy. ![]() Output from colorizing black & white (made with colorize-it)Īs we see above, the output is not identical to the input, but the network does a fairly decent job.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |