在监督对比学习(Supervised Contrastive Learning, SCL)中,我们旨在利用标签信息来增强特征表示的学习。以下是一个基于PyTorch实现监督对比学习的步骤指南,包括数据集准备、数据加载与预处理、模型结构、损失函数以及训练过程的概述。 1. 查找或构建适用于监督对比学习的数据集 对于监督对比学习,我们需要一个包含标签的数据...
python main_ce.py --batch_size 1024 \ --learning_rate 0.8 \ --cosine --syncBN \ (2) Supervised Contrastive Learning Pretraining stage: python main_supcon.py --batch_size 1024 \ --learning_rate 0.5 \ --temp 0.1 \ --cosine
raymin0223/self-contrastive-learning Star20 Self-Contrastive Learning: Single-viewed Supervised Contrastive Framework using Sub-network (AAAI 2023) pytorchcontrastive-learningsupervised-contrastive-learningmulti-exit-architecturessingle-viewed-contrastive
方法:为了解决上述问题,本文提出了一种名为Targeted Supervised Contrastive Learning (TSC) 的方法,提高了超球上特征分布的uniformity。 Code: LTH14/targeted-supcon: A PyTorch implementation of the paper Targeted Supervised Contrastive Learning for Long-tailed Recognition (github.com) 图中,随着不平衡比例增加...
van den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. CoRR abs/1807.03748 (2018).http://arxiv.org/abs/1807.03748 Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library.https://arxiv.org/pdf/1912.01703 ...
code:KimMeen/SL-GAD: [TKDE 2021] A PyTorch implementation of "Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection". (github.com) Abstract 提到challenges 现有的数据挖掘和机器学习方法要么是浅层方法,不能有效捕获复杂的图数据相互依赖关系; 要么是图自动编码方法,不能充分利用上下...
Meanwhile, contrastive loss has been considered instead of the traditional cross-entropy loss in a variety of machine learning applications, showing to be more robust for system stability alternative in self-supervised learning. Following this trend, we hypothesise that using a supervised contrastive ...
python main_ce.py --batch_size 1024 \ --learning_rate 0.8 \ --cosine --syncBN \ (2) Supervised Contrastive Learning Pretraining stage: python main_supcon.py --batch_size 1024 \ --learning_rate 0.5 \ --temp 0.1 \ --cosine
This is a code demo for the paper "Supervised Contrastive Learning-Based Unsupervised Domain Adaptation for Hyperspectral Image Classification"RequirementsCUDA = 11.4Python = 3.9Pytorch = 1.10.0sklearn = 1.0.1numpy = 1.21.2cleanlab = 1.0dataset...
其中,Contrastive learning 的范式又叫做 non-parametric instance discrimination,例如 SimCLR 和 MoCo。non-parametric instance discrimination 一般采用双分支的结构。双分支的结构存在 information leakage 的问题,因此需要一些特殊的设计来解决这种问题,如:Momentum Encoder (MoCo),特殊 BN 层 (MoCo),limited negative pa...