5 个仓库
Awesome GitHub RepositoriesAutoencoder Compression
Using autoencoder architectures to compress input data into lower-dimensional latent representations.
Distinct from Neural Network Model Compression: Distinct from general model compression (pruning/quantization) as it refers to the architectural compression of data via latent spaces.
Explore 5 awesome GitHub repositories matching artificial intelligence & ml · Autoencoder Compression. Refine with filters or upvote what's useful.
Awesome Autoencoder Compression GitHub Repositories
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- pkmital/tensorflow_tutorials
pkmital/tensorflow_tutorials
5,668在 GitHub 上查看这是一个教育性 Jupyter Notebook 合集，提供了使用 TensorFlow 框架构建神经网络和进行张量运算的教程。它作为机器学习教育仓库和深度学习学生的实现指南。 该套件专注于特定的高级架构，包括用于图像分类的卷积神经网络、用于训练稳定性的残差网络（带跳跃连接），以及用于生成建模和数据合成的变分自编码器。它还包含构建去噪和深度自编码器以进行特征提取和降维的指南。 该仓库涵盖了更广泛的预测建模领域，实现了用于预测连续值和二元结果的线性、多项式和逻辑回归。 内容组织为交互式 Notebook，允许用户执行数学运算并修改机器学习实验。
Provides implementations of autoencoders to compress input data into lower-dimensional latent representations.
Jupyter Notebook - binroot/tensorflow-book
BinRoot/TensorFlow-Book
4,431在 GitHub 上查看这是一个 TensorFlow 机器学习示例集合，为各种神经网络范式提供了参考实现。它涵盖了监督学习、无监督学习、强化学习和序列学习模型。 该仓库包含了专注于图像分类和排序的卷积神经网络实现，以及用于时间序列预测和序列到序列翻译的循环神经网络。此外，它还提供了通过奖励优化训练的强化学习智能体，以及用于数据聚类的自编码器和自组织映射等无监督学习技术。 其他功能涵盖了监督回归和分类、语义嵌入生成，以及用于序列数据建模的隐马尔可夫模型。该项目还包括用于张量操作管理和通过仪表板进行模型性能可视化的实用工具。 内容以一系列 Jupyter Notebook 的形式提供。
Uses autoencoder architectures to compress image data into lower-dimensional latent representations for noise removal.
Jupyter Notebookautoencoderbookclassification - commaai/research
commaai/research
4,121在 GitHub 上查看This research framework provides a deep learning driving simulator and a multimodal data pipeline for autonomous vehicle research. It centers on the creation of synchronized autonomous vehicle datasets, which combine high-frequency vehicle telemetry with camera frames to train neural networks. The project implements a convolutional neural network trainer specifically designed to predict steering angles and vehicle transition states from visual data. It features generative capabilities, using autoencoders and transition models to synthesize driving environments and simulate future vehicle move
Uses autoencoders to learn compressed representations of driving data for image synthesis and model checkpoints.
Python - divamgupta/stable-diffusion-tensorflow
divamgupta/stable-diffusion-tensorflow
1,611在 GitHub 上查看This project provides a TensorFlow implementation of the Stable Diffusion model, serving as a generative engine for creating and modifying visual content. It functions as a machine learning architecture that translates natural language descriptions into high-quality images by iteratively refining noise within a compressed latent space. The system enables a variety of generative tasks, including text-to-image synthesis, image inpainting to fill missing or masked regions, and image editing to transform existing visuals based on text prompts. Beyond static imagery, the framework supports the gen
Compresses high-resolution pixel data into compact latent representations to reduce computational overhead.
Python - glouppe/info8010-deep-learning
glouppe/info8010-deep-learning
1,291在 GitHub 上查看This project provides a comprehensive educational curriculum and research resource for deep learning, focusing on the theoretical and technical foundations of neural network implementation. It serves as a structured academic guide for building and training complex models from scratch, covering the essential mathematical primitives, computational graph construction, and automatic differentiation mechanisms required for modern machine learning. The repository distinguishes itself through its extensive coverage of generative modeling and specialized neural architectures. It includes practical im
Reduces high-dimensional input data into compact latent representations using autoencoder architectures.
Jupyter Notebook
