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Originally developed using Google's own Imagen text-to-image model, DreamBooth implementations can be applied to other text-to-image models, where it can allow the model to generate more fine-tuned and personalized outputs after training on three to five images of a subject. [1] [2] [3]
List of datasets in computer vision and image processing; ... TensorFlow Lite has APIs for mobile apps or embedded devices to generate and deploy TensorFlow models. [63]
In 2016, Reed, Akata, Yan et al. became the first to use generative adversarial networks for the text-to-image task. [5] [7] With models trained on narrow, domain-specific datasets, they were able to generate "visually plausible" images of birds and flowers from text captions like "an all black bird with a distinct thick, rounded bill".
Aerial Image Segmentation Dataset 80 high-resolution aerial images with spatial resolution ranging from 0.3 to 1.0. Images manually segmented. 80 Images Aerial Classification, object detection 2013 [156] [157] J. Yuan et al. KIT AIS Data Set Multiple labeled training and evaluation datasets of aerial images of crowds.
The generator is decomposed into a pyramid of generators =, with the lowest one generating the image () at the lowest resolution, then the generated image is scaled up to (()), and fed to the next level to generate an image (+ (())) at a higher resolution, and so on. The discriminator is decomposed into a pyramid as well.
During training, at first only , are used in a GAN game to generate 4x4 images. Then G N − 1 , D N − 1 {\displaystyle G_{N-1},D_{N-1}} are added to reach the second stage of GAN game, to generate 8x8 images, and so on, until we reach a GAN game to generate 1024x1024 images.
DeepSeek’s Janus-Pro is free-to-use without restriction, unlike image generators built by US rivals like OpenAI and Stability AI. It also marks a breakthrough in the way such models are trained ...
U-Net is a convolutional neural network that was developed for image segmentation. [1] The network is based on a fully convolutional neural network [2] whose architecture was modified and extended to work with fewer training images and to yield more precise segmentation.