Explore >> Select a destination
|
You are here 
|
research.google | ||
| | | | |
coen.needell.org
|
|
| | | | | In my last post on computer vision and memorability, I looked at an already existing model and started experimenting with variations on that architecture. The most successful attempts were those that use Residual Neural Networks. These are a type of deep neural network built to mimic specific visual structures in the brain. ResMem, one of the new models, uses a variation on ResNet in its architecture to leverage that optical identification power towards memorability estimation. M3M, another new model, ex... | |
| | | | |
www.v7labs.com
|
|
| | | | | A list of computer vision datasets, including image classification, object detection, and semantic segmentation. | |
| | | | |
deepmind.google
|
|
| | | | | According to empirical evidence from prior works, utility degradation in DP-SGD becomes more severe on larger neural network models - including the ones regularly used to achieve the best... | |
| | | | |
www.nicktasios.nl
|
|
| | | In the Latent Diffusion Series of blog posts, I'm going through all components needed to train a latent diffusion model to generate random digits from the MNIST dataset. In the second post, we will bu | ||
