Showing 79 open source projects for "project"

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    PyTorch GAN Zoo

    PyTorch GAN Zoo

    A mix of GAN implementations including progressive growing

    PyTorch GAN Zoo is a comprehensive open research toolbox designed for experimenting with and developing Generative Adversarial Networks (GANs) using PyTorch. The project provides modular implementations of popular GAN architectures, including Progressive Growing of GANs (PGAN), DCGAN, and an experimental StyleGAN version. It is built to support both researchers and developers who want to train, evaluate, and extend GANs efficiently across diverse datasets such as CelebA-HQ, FashionGen, DTD, and CIFAR-10. ...
    Downloads: 2 This Week
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  • 2
    MAML-Pytorch

    MAML-Pytorch

    Elegant PyTorch implementation of paper Model-Agnostic Meta-Learning

    ...The repository supports MiniImagenet and Omniglot, two common benchmark datasets for meta-learning experiments. It includes separate training scripts, dataset loaders, learner components, and meta-learning logic. The project also notes that MAML can be difficult to train and presents the implementation as a practical starting point for research. Overall, it is useful for students and researchers who want to study fast adaptation, few-shot classification, and gradient-based meta-learning in PyTorch.
    Downloads: 0 This Week
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  • 3
    Improved GAN

    Improved GAN

    Code for the paper "Improved Techniques for Training GANs"

    ...Although the project is archived and not actively maintained, it remains a reference point in the history of GAN research, influencing subsequent model training approaches.
    Downloads: 1 This Week
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  • 4
    Retrieval-Based Conversational Model

    Retrieval-Based Conversational Model

    Dual LSTM Encoder for Dialog Response Generation

    Retrieval-Based Conversational Model in Tensorflow is a project implementing a retrieval-based conversational model using a dual LSTM encoder architecture in TensorFlow, illustrating how neural networks can be trained to select appropriate responses from a fixed set of candidate replies rather than generate them from scratch. The core idea is to embed both the conversation context and potential replies into vector representations, then score how well each candidate fits the current dialogue, choosing the best match accordingly. ...
    Downloads: 0 This Week
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