Showing 2 open source projects for "javaee code generator"

View related business solutions
  • Host LLMs in Production With On-Demand GPUs Icon
    Host LLMs in Production With On-Demand GPUs

    NVIDIA L4 GPUs. 5-second cold starts. Scale to zero when idle.

    Deploy your model, get an endpoint, pay only for compute time. No GPU provisioning or infrastructure management required.
    Start Free
  • Veeam Data Platform v13.1 Icon
    Veeam Data Platform v13.1

    Move workloads across hypervisors and clouds with no vendor lock-in. Try VDP free today.

    Try Veeam Data Platform today. Experience the unified platform that's secure by design, portable by default, and proven to recover clean, fast, and anywhere.
    Try Now
  • 1
    InfoGAN

    InfoGAN

    Code for reproducing key results in the paper

    ...InfoGAN is a variant of the GAN (Generative Adversarial Network) architecture that aims to learn disentangled and interpretable latent representations by maximizing the mutual information between a subset of the latent codes and the generated outputs. That extra incentive encourages the generator to structure its latent space in a way where certain latent variables control meaningful, distinct factors (e.g. rotation, width, stroke thickness) in the output images. The repository includes code for experiments (e.g. on MNIST), launcher scripts, and some tests. It depends on a development version of TensorFlow (the code expects features not in older stable releases), and also uses other libraries like prettytensor and progressbar.
    Downloads: 0 This Week
    Last Update:
    See Project
  • 2
    SG2Im

    SG2Im

    Code for "Image Generation from Scene Graphs", Johnson et al, CVPR 201

    ...This separation lets the model reason about geometry and composition before committing to texture and color, improving spatial fidelity. The repository includes training code, datasets, and evaluation scripts so researchers can reproduce baselines and extend components such as the graph encoder or image generator. In practice, sg2im demonstrates how structured semantics can guide generative models to produce controllable, compositional imagery.
    Downloads: 0 This Week
    Last Update:
    See Project
  • Previous
  • You're on page 1
  • Next