Showing 5 open source projects for "linux space invaders"

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  • 1
    vJEPA-2

    vJEPA-2

    PyTorch code and models for VJEPA2 self-supervised learning from video

    VJEPA2 is a next-generation self-supervised learning framework for video that extends the “predict in representation space” idea from i-JEPA to the temporal domain. Instead of reconstructing pixels, it predicts the missing high-level embeddings of masked space-time regions using a context encoder and a slowly updated target encoder. This objective encourages the model to learn semantics, motion, and long-range structure without the shortcuts that pixel-level losses can invite. The...
    Downloads: 2 This Week
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  • 2
    JEPA

    JEPA

    PyTorch code and models for V-JEPA self-supervised learning from video

    JEPA (Joint-Embedding Predictive Architecture) captures the idea of predicting missing high-level representations rather than reconstructing pixels, aiming for robust, scalable self-supervised learning. A context encoder ingests visible regions and predicts target embeddings for masked regions produced by a separate target encoder, avoiding low-level reconstruction losses that can overfit to texture. This makes learning focus on semantics and structure, yielding features that transfer well...
    Downloads: 2 This Week
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  • 3
    TorchIO

    TorchIO

    Medical imaging toolkit for deep learning

    TorchIO is an open-source Python library for efficient loading, preprocessing, augmentation and patch-based sampling of 3D medical images in deep learning, following the design of PyTorch. It includes multiple intensity and spatial transforms for data augmentation and preprocessing. These transforms include typical computer vision operations such as random affine transformations and also domain-specific ones such as simulation of intensity artifacts due to MRI magnetic field inhomogeneity...
    Downloads: 1 This Week
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  • 4
    RWKV

    RWKV

    RNN with great LLM performance

    RWKV-LM is the main research and training repository for the RWKV language model architecture. It presents RWKV as an attention-free RNN-style model that aims to reach transformer-level language model performance. The project is built around the idea that a model can be trained in a parallelizable way like a GPT-style transformer while running inference with recurrent efficiency. This gives RWKV important advantages for long-context use, including lower memory pressure and no traditional...
    Downloads: 0 This Week
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  • 5
    fastMRI

    fastMRI

    A large open dataset + tools to speed up MRI scans using ML

    fastMRI is a large-scale collaborative research project by Facebook AI Research (FAIR) and NYU Langone Health that explores how deep learning can accelerate magnetic resonance imaging (MRI) acquisition without compromising image quality. By enabling reconstruction of high-fidelity MR images from significantly fewer measurements, fastMRI aims to make MRI scanning faster, cheaper, and more accessible in clinical settings. The repository provides an open-source PyTorch framework with data...
    Downloads: 0 This Week
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