Showing 3 open source projects for ".net code generator"

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  • 1
    Demucs

    Demucs

    Code for the paper Hybrid Spectrogram and Waveform Source Separation

    Demucs (Deep Extractor for Music Sources) is a deep-learning framework for music source separation—extracting individual instrument or vocal tracks from a mixed audio file. The system is based on a U-Net-like convolutional architecture combined with recurrent and transformer elements to capture both short-term and long-term temporal structure. It processes raw waveforms directly rather than spectrograms, allowing for higher-quality reconstruction and fewer artifacts in separated tracks. The...
    Downloads: 104 This Week
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  • 2
    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
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  • 3
    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
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