George Lakoff的语言理论发展经历了几次重要的转向,从对转换生成语法的热心支持到提出生成语义学,最后发展到认知语义学.我们认为,这些转向不是偶然的,对句法和语义关系做出合理解释的理想和外部学术环境的影响是促使Lakoff理论创新的...George Lakoff的语言理论发展经历了几次重要的转向,从对转换生成语法的热心支持到提出生成语义学,最后发展到认知语义学.我们认为,这些转向不是偶然的,对句法和语义关系做出合理解释的理想和外部学术环境的影响是促使Lakoff理论创新的动因.认知语义学作为对标准理论和生成语义学的最新发展,其理论优势在于它对语义现象的研究更符合语言实际和人类心智的规律.展开更多
Unsupervised image translation(UIT)studies the mapping between two image domains.Since such mappings are under-constrained,existing research has pursued various desirable properties such as distributional matching or ...Unsupervised image translation(UIT)studies the mapping between two image domains.Since such mappings are under-constrained,existing research has pursued various desirable properties such as distributional matching or two-way consistency.In this paper,we re-examine UIT from a new perspective:distributional semantics consistency,based on the observation that data variations contain semantics,e.g.,shoes varying in colors.Further,the semantics can be multi-dimensional,e.g.,shoes also varying in style,functionality,etc.Given two image domains,matching these semantic dimensions during UIT will produce mappings with explicable correspondences,which has not been investigated previously.We propose distributional semantics mapping(DSM),the first UIT method which explicitly matches semantics between two domains.We show that distributional semantics has been rarely considered within and beyond UIT,even though it is a common problem in deep learning.We evaluate DSM on several benchmark datasets,demonstrating its general ability to capture distributional semantics.Extensive comparisons show that DSM not only produces explicable mappings,but also improves image quality in general.展开更多
基金supported by National Natural Science Foundation of China(Grant No.61772462)the 100 Talents Program of Zhejiang University。
文摘Unsupervised image translation(UIT)studies the mapping between two image domains.Since such mappings are under-constrained,existing research has pursued various desirable properties such as distributional matching or two-way consistency.In this paper,we re-examine UIT from a new perspective:distributional semantics consistency,based on the observation that data variations contain semantics,e.g.,shoes varying in colors.Further,the semantics can be multi-dimensional,e.g.,shoes also varying in style,functionality,etc.Given two image domains,matching these semantic dimensions during UIT will produce mappings with explicable correspondences,which has not been investigated previously.We propose distributional semantics mapping(DSM),the first UIT method which explicitly matches semantics between two domains.We show that distributional semantics has been rarely considered within and beyond UIT,even though it is a common problem in deep learning.We evaluate DSM on several benchmark datasets,demonstrating its general ability to capture distributional semantics.Extensive comparisons show that DSM not only produces explicable mappings,but also improves image quality in general.
