4 projects for "visual-" with 2 filters applied:

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  • 1
    PyTorch3D

    PyTorch3D

    PyTorch3D is FAIR's library of reusable components for deep learning

    ...The library provides fast GPU-accelerated implementations of rendering pipelines, transformations, rasterization, and lighting—making it possible to compute gradients through full 3D rendering processes. Researchers use it for tasks like shape generation, reconstruction, view synthesis, and visual reasoning. PyTorch3D also includes utilities for loading, transforming, and sampling 3D assets, so models can be trained end-to-end from 2D supervision or partial data. Its modular design allows easy extension—components like differentiable rasterizers, mesh blending, or signed distance field (SDF) modules can be swapped or combined to test new architectures quickly.
    Downloads: 1 This Week
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  • 2
    Exposure Correction

    Exposure Correction

    Learning multi-scale deep model correcting over- and under- exposed

    ...The repository includes pre-trained models, datasets, and training/testing code to enable reproducibility and experimentation. By leveraging this framework, researchers and developers can apply exposure correction to a wide range of natural images, improving visual quality without manual editing. The project serves both as a research reference and a practical tool for computational photography and image enhancement.
    Downloads: 2 This Week
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  • 3
    Deep Learning cheatsheets

    Deep Learning cheatsheets

    VIP cheatsheets for Stanford's CS 230 Deep Learning

    Deep Learning cheatsheets forStanford's CS 230 is an educational repository that compiles comprehensive cheat sheets, summaries, and study resources covering the core concepts taught in Stanford’s CS230 Deep Learning course. The project organizes complex machine learning topics into visually structured reference materials that simplify studying neural networks, convolutional architectures, recurrent networks, optimization strategies, and training methodologies. It was created to help...
    Downloads: 0 This Week
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  • 4
    distribution-is-all-you-need

    distribution-is-all-you-need

    The basic distribution probability Tutorial for Deep Learning Research

    distribution-is-all-you-need is a Python-based probability tutorial aimed at deep learning researchers and students. It explains common discrete and continuous distributions through short scripts, formulas, descriptions, and plotted graphs. Covered topics include uniform, Bernoulli, binomial, categorical, multinomial, beta, Dirichlet, gamma, exponential, Gaussian, normal, chi-squared, and Student's t distributions. The material highlights relationships such as conjugate priors and...
    Downloads: 1 This Week
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