Showing 2 open source projects for "convolution"

View related business solutions
  • Compliant and Reliable File Transfers Backed by Top Security Certifications Icon
    Compliant and Reliable File Transfers Backed by Top Security Certifications

    Cerberus FTP Server delivers SOC 2 Type II certified security and FIPS 140-2 validated encryption.

    Stop relying on non-certified, legacy file transfer tools that creak under the weight of modern security demands. Get full audit trails, advanced access controls and more supported by an award-winning team of experts. Start your free 25-day trial today.
    Start Free Trial
  • $300 Free Credits to Build on Google Cloud Icon
    $300 Free Credits to Build on Google Cloud

    New to Google Cloud? Get $300 in credits to explore Compute Engine, BigQuery, Cloud Run, Gemini Enterprise Agent Platform, and more.

    Start your next project with $300 in free Google Cloud credit. Spin up VMs, run containers, query petabytes in BigQuery, or build agents with Gemini Enterprise Agent Platform. Once your credits are used, keep building with 20+ always-free tier products including Compute Engine, Cloud Storage, GKE, and Cloud Run functions. No commitment required—just sign up and start building.
    Claim $300 Free
  • 1
    Make-A-Video - Pytorch (wip)

    Make-A-Video - Pytorch (wip)

    Implementation of Make-A-Video, new SOTA text to video generator

    ...The gist of the paper comes down to, take a SOTA text-to-image model (here they use DALL-E2, but the same learning points would easily apply to Imagen), make a few minor modifications for attention across time and other ways to skimp on the compute cost, do frame interpolation correctly, get a great video model out. Passing in images (if one were to pretrain on images first), both temporal convolution and attention will be automatically skipped. In other words, you can use this straightforwardly in your 2d Unet and then port it over to a 3d Unet once that phase of the training is done.
    Downloads: 4 This Week
    Last Update:
    See Project
  • 2
    texturize

    texturize

    Generate photo-realistic textures based on source images

    ...It's useful in the context of computer graphics if you want to make variations on a theme or expand the size of an existing texture. This software is powered by deep learning technology, using a combination of convolution networks and example-based optimization to synthesize images. We're building texturize as the highest-quality open source library available! The examples are available as notebooks, and you can run them directly in-browser thanks to Jupyter and Google Colab.
    Downloads: 1 This Week
    Last Update:
    See Project
  • Previous
  • You're on page 1
  • Next
Auth0 Logo