synthetic data

Check out the FilmGeeks3 Collection In last year’s post on generative models, we showcased theatrical posters synthesized with GANs and diffusion models. Since that time, Latent Diffusion has gained popularity for faster training while allowing the output to be conditioned on text and images. This variant performs diffusion in the embedding space after encoding text or image inputs with CLIP. By allowing the generated output to be conditioned on text and/or image inputs, the user has much more influence on the results....

While prototyping YogAI, our smart mirror fitness application, we dreamed of using generative models like GANs to render realistic avatars. For the TFWorld 2.0 Challenge, we came a bit closer to that vision by demonstrating a pipeline which quickly creates motion transfer videos. More recently, we have been learning about reconstruction techniques and have been excited about the work around Neural Radiance Fields (Nerf). By this method, one learns an implicit representation of a scene from posed monocular videos....

Sometimes, we succeed applying transfer learning with relatively few labeled samples to develop custom models. However, there are times when the cost of acquisition is so great that even having a few examples to learn from is difficult. Scientists curate databases like FathomNet to share expertise about the ocean’s wildlife. Applying machine learning to classify marine species is quite challenging in practice due in part to rarity of encounters and challenging photographic environments....