4 个仓库
Awesome GitHub RepositoriesStochastic Depth Regularization
Regularization techniques that randomly drop network paths during training to prevent overfitting.
Distinct from Overfitting Reduction Techniques: Specifically implements stochastic depth as a path-dropping regularization, distinct from general data-driven overfitting reduction.
Explore 4 awesome GitHub repositories matching artificial intelligence & ml · Stochastic Depth Regularization. Refine with filters or upvote what's useful.
Awesome Stochastic Depth Regularization GitHub Repositories
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- facebookresearch/convnext
facebookresearch/ConvNeXt
6,388在 GitHub 上查看Code release for ConvNeXt model
Applies stochastic depth regularization by randomly dropping residual blocks during training.
Python - mosaicml/composer
mosaicml/composer
5,485在 GitHub 上查看Composer 是一个 PyTorch 分布式训练框架，旨在实现大规模模型在多节点 GPU 集群上的扩展。它兼具大语言模型训练器、分布式模型优化器和训练生命周期管理器的功能。 该项目作为深度学习正则化库脱颖而出，提供诸如 Sharpness Aware Minimization、MixUp 和 CutMix 等专业优化技术，以提升模型的泛化能力。它还通过序列长度预热、渐进式层冻结以及用于大规模模型恢复的分片状态检查点技术，优化了训练流程。 该框架涵盖了广泛的功能领域，包括分布式训练编排、混合精度硬件管理和云原生数据流。它还为 GPU 内存诊断、训练发散检测和吞吐量跟踪提供了丰富的监控与可观测性工具。 该项目包含一个命令行启动器，可自动执行跨节点的分布式多 GPU 训练任务。
Provides stochastic depth regularization to randomly drop network paths during training and prevent overfitting in deep models.
Python - ai-dawang/plugnplay-modules
ai-dawang/PlugNPlay-Modules
4,968在 GitHub 上查看PlugNPlay-Modules is a collection of reusable PyTorch computer vision modules and deep learning architectural components. It provides a library of standardized building blocks for constructing neural networks, focusing on attention mechanisms, signal processing layers, and feature fusion modules. The project is distinguished by its extensive variety of attention primitives, covering spatial, channel, and temporal weighting, as well as specialized variants like deformable, frequency-enhanced, and linear-complexity attention. It also implements advanced signal processing tools within the neural
Implements stochastic depth to randomly drop sample paths during training to improve model generalization.
Python - lightly-ai/lightly
lightly-ai/lightly
3,684在 GitHub 上查看Lightly is a self-supervised learning framework and computer vision data curation tool designed to manage large image datasets and train models on unlabeled data. It functions as a PyTorch vision library and dataset management SDK, providing tools to convert raw images into high-dimensional vectors for similarity search, visualization, and feature extraction. The project implements a variety of self-supervised architectures, including MoCo, SimCLR, VICReg, Barlow Twins, and masked image modeling. It distinguishes itself by combining these learning frameworks with active learning capabilities,
Injects dropout into transformer blocks via stochastic depth regularization to prevent overfitting during training.
Pythoncomputer-visioncontrastive-learningcontributions-welcome
