两步预训练阶段将最终生成一个模型,该模型可作为随后对标记数据进行微调阶段的初始化,以提高分割的准确性。 2.2 Supervised Local Contrastive learning 设f l(xi) = h2(Dl(E(ai))是增强输入ai的第l个最上层解码器块Dl的输出特征映射,其中head h2是一个两层逐点卷积。对于feature map f (ai),局部对比损耗定...
semi-supervised contrastive learning训练 Semi-supervised contrastive learning is a training approach used in machine learning to leverage labeled and unlabeled data in a semi-supervised setting. In contrastive learning, the goal is to learn representations (embeddings) of data points such that similar ...
Machine LearningSatellite ImagerySemi-Supervised LearningContrastive LearningArchaeology has long faced fundamental issues of sampling and scalar representation. Traditionally, the local-to-regional-scale views of settlement patterns are produced through systematic pedestrian surveys. Recently, systematic manual ...
论文链接:Contrastive Semi-Supervised Learning for Underwater Image Restoration via Reliable Bank (thecvf.com) 0 摘要 摘要中作者提到了一个目前深度学习普遍遇到的问题,带标注的数据太少了,针对这个问题作者提出了以一个基于半监督方法和mean-teacher的水下图像恢复模型,其实自监督、半监督方法一个重要的应用场景...
Zhang G, Hu Z, Wen G, et al. Dynamic graph convolutional networks by semi-supervised contrastive learning[J]. Pattern Recognition, 2023, 139: 109486. 摘要导读 传统的图卷积网络(GCN)及其变体通常只通过数据集给出的拓扑结构传播节点信息。然而,给定的拓扑结构只能表示一定的关系,而忽略节点之间的一些相关...
3. 对比学习(Contrastive Learning):寻找食材的家族相似性 4. 伪标签(Pseudo Labeling):假装你是大厨 5. 半监督序列学习(Semi-supervised Sequence Learning) 四、半监督学习的训练目标 1. 提高模型的泛化能力 2. 利用未标注数据挖掘深层信息 3. 减少人工标注的需求 ...
To address these problems, we introduce a novel semi-supervised training strategy incorporating contrastive learning. It allows for editing real-scene text images with any text, circumventing the identity mapping issue while ensuring the accuracy of both text content and style. Moreover, we propose ...
5. 半监督序列学习(Semi-supervised Sequence Learning) 在自然语言处理(NLP)领域,半监督序列学习方法,如BERT和其它变体,通过预训练模型在大量未标注文本上学习语言表示,然后在少量标注数据上进行微调,用于特定的下游任务,如情感分析或问答系统。 在处理文本或语音数据时,半监督序列学习方法就像是掌握了一本包含秘密调味...
Wei F, Chen Z, Hao Z, et al. Semi-Supervised Clustering with Contrastive Learning for Discovering New Intents[J]. arXiv preprint arXiv:2201.07604, 2022. 摘要导读 现实世界中的大多数对话系统都依赖于预定义的意图和答案,因此从现有的大型语料库中发现潜在的意图对于构建这样的对话服务非常重要。考虑到...
including the limited label informa-tion in the pre-training phase, it is possible to boost the performance ofcontrastive learning. We propose a supervised local contrastive loss thatleverages limited pixel-wise annotation to force pixels with the same la-bel to gather around in the embedding ...