analysis How AI agents will transform the future of work Dec 03, 202412 mins analysis How to transform your architecture review board Nov 19, 20247 mins analysis How to support accurate revenue forecasting with data science and dataops Nov 05, 20248 mins ...
Zero-shot learning for visual recognition has received much interest in the most recent years. However, the semantic gap across visual features and their u... Z Ding,S Ming,F Yun - IEEE Conference on Computer Vision & Pattern Recognition 被引量: 18发表: 2017年 Ontology-Based Generalized Zero...
论文链接:http://openaccess.thecvf.com/content_CVPR_2019/papers/Huang_Generative_Dual_Adversarial_Network_for_Generalized_Zero-Shot_Learning_CVPR_2019_paper.pdf 创新:论文提出了一个新颖的模型,该模型为三种不同的方法提供了统一的框架:视觉→语义映射,语义→视觉映射和深度度量学习。 GDAN网络模型 模型包含...
Graph-based variational auto-encoder for generalized zero-shot learning Zero-shot learning has been a highlighted research topic in both vision and language areas. Recently, generative methods have emerged as a new trend of zero-shot learning, which synthesizes unseen categories samples via generative...
These queries/prompts are examples of 'zero-shot' prompts, the expectation being a good result with just one query. As opposed to a back-and-forth chat session working towards a desired outcome. I wonder how many attempts everyone tries before they decide they can't anything more from the ...
Alleviating Feature Confusion for Generative Zero-shot Learning | Proceedings of the 27th ACM International Conference on Multimediadl.acm.org/doi/10.1145/3343031.3350901 摘要: 基于GAN的ZSL方法,生成的未知类往往倾向于和已知类非常相似,而不能反应未知类的独特性和多样性。人们很难区分合成出来的未知类和...
在没有见过数据的zero-shot任务中,GPT-1的模型要比基于LSTM的模型稳定,且随着训练次数的增加,GPT-1的性能也逐渐提升,表明GPT-1有非常强的泛化能力,能够用到和有监督任务无关的其它NLP任务中。 GPT-1证明了transformer对学习词向量的强大能力,在GPT-1得到的词向量基础上进行下游任务的学习,能够让下游任务取得更好...
(e.g. discriminative, abductive, ending prediction). To enhance knowledge transfer and enable zero-shot generalization among various combinations, in this work we propose a novel unified framework, called UNIEVENT. Inspired by prefix-based multitask learning, ...
In our experiments, we consider the tasks of multi-class classification, multi-label classification, and zero-shot learning. We show that our GDVM performs favorably against the baselines or recent generative DNN models. 展开 关键词: Computer Science - Machine Learning ...
零样本图像识别 | Alleviating Feature Confusion for Generative Zero-shot Learning简单论文笔记 提出特征混淆问题,即在GZSL设置中零样本类别实例容易被划分为可见类(因为在训练生成器时使用的是可见类样本,这导致生成的未见类样本与可见类相似) 创新点:提出了一种边界损失函数,该损失函数最大程度地减少了已见类别和...