该映射用于为冷启动用户生成模拟行为序列,然后先进的顺序推荐模型利用生成的序列为冷启动用户提供推荐。 该模型由三个主要模块组成:数据增强模块、跨模态对比学习模块和顺序推荐模块。 数据增强模块采用对比学习来增强用户特征和用户 - 项目交互序列,促使模型在嵌入空间中使相似用户的嵌入更接近,以提取更丰富的隐藏特征。
此外,与 SimCLR [7] 类似,CrossPoint 不需要用于负采样的存储体。尽管有记忆库,但丰富的增强和硬阳性样本的制定已被证明可以促进对比学习[25,77]。我们假设模态内设置和跨模态对应中所采用的变换提供了足够的特征增强。特别是,渲染的 2D 图像特征可以作为制定更好的表示学习的硬性肯定。 我们通过多个下游任务验证了...
"application", 介绍相关概念与研究背景;第二、三篇会侧重"algorithm" 介绍这个方向研究的技术路线,其中第二篇介绍基于 GAN 的追求公共子空间的 cross-modal 检索;第三篇则从 modal 抽象成更一般的 domain,并且将多域扩展到单域,总结分析单/多域匹配问题,主要介绍基于 contrastive learning / instances ...
"application", 介绍相关概念与研究背景;第二、三篇会侧重"algorithm" 介绍这个方向研究的技术路线,其中第二篇介绍基于 GAN 的追求公共子空间的 cross-modal 检索;第三篇则从 modal 抽象成更一般的 domain,并且将多域扩展到单域,总结分析单/多域匹配问题,主要介绍基于 contrastive learning / instances ...
几篇论文实现代码:《Cross-Modal Contrastive Learning for Text-to-Image Generation》(CVPR 2021) GitHub:https:// github.com/google-research/xmcgan_image_generation 《DANNet: A One-Stage Domain Adapt...
Effective protein representation learning is crucial for predicting protein functions. Traditional methods often pretrain protein language models on large, unlabeled amino acid sequences, followed by finetuning on labeled data. While effective, these met
In this paper, we propose a novel framework, Cross-Modal Contrastive Learning (CMCL), which integrates multiple contrastive learning methods and multimodal data augmentation to address the heterogeneity issue. Specifically, we establish a cross-modal contrastive learning framework by leveraging diversity ...
XMC-GAN uses an attentional self-modulation generator, which enforces strong text-image correspondence, and a contrastive discriminator, which acts as a critic as well as a feature encoder for contrastive learning. The quality of XMC-GAN's output is a major step up from previous models, as ...
We note that adopting such cross-modal contrastive learning between 2D images and 3D shapes into IBSR tasks is non-trivial and challenging: contrastive learning requires very strong data augmentation in constructed positive pairs to learn the feature invariance, whereas traditional metric learning works ...
耦合方法(ensemble method)是一种缓和Q-学习(Q-learning)过估计(overestimation)问题的方法。通常我们使用多个Q-函数估计器来估计函数值。众所周知,估计偏差在很大程度上取决于耦合大小(即目标中使用的 Q 函数估计器的数量。考虑到训练过程中函数估计误差的时变特征, 如何确定“正确”的耦合大小非常重要。为了应对这一...