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Multi-Label Transfer Learning for Multi-Relational Semantic Similarity [article]

Li Zhang, Steven R. Wilson, Rada Mihalcea
2019 arXiv   pre-print
We propose a multi-label transfer learning approach based on LSTM to make predictions for several relations simultaneously and aggregate the losses to update the parameters.  ...  Multi-relational semantic similarity datasets define the semantic relations between two short texts in multiple ways, e.g., similarity, relatedness, and so on.  ...  Acknowledgments This material is based in part upon work supported by the Michigan Institute for Data Science, by the John Templeton Foundation (grant #61156), by the National Science Foundation (grant  ... 
arXiv:1805.12501v2 fatcat:3um7lbyqfzcevnaaivp4txjb3e

Multi-Label Transfer Learning for Multi-Relational Semantic Similarity

Li Zhang, Steven Wilson, Rada Mihalcea
2019 Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*  
We propose a multi-label transfer learning approach based on LSTM to make predictions for several relations simultaneously and aggregate the losses to update the parameters.  ...  Multi-relational semantic similarity datasets define the semantic relations between two short texts in multiple ways, e.g., similarity, relatedness, and so on.  ...  Acknowledgments This material is based in part upon work supported by the Michigan Institute for Data Science, by the John Templeton Foundation (grant #61156), by the National Science Foundation (grant  ... 
doi:10.18653/v1/s19-1005 dblp:conf/starsem/ZhangWM19 fatcat:ozugcxpmmrft3hhfjsntsoqy74

Learning Semantic Similarity for Multi-label Text Categorization [chapter]

Li Li, Mengxiang Wang, Longkai Zhang, Houfeng Wang
2014 Lecture Notes in Computer Science  
We propose a multi-label transfer learning approach based on LSTM to make predictions for several relations simultaneously and aggregate the losses to update the parameters.  ...  Multi-relational semantic similarity datasets define the semantic relations between two short texts in multiple ways, e.g., similarity, relatedness, and so on.  ...  Acknowledgments This material is based in part upon work supported by the Michigan Institute for Data Science, by the John Templeton Foundation (grant #61156), by the National Science Foundation (grant  ... 
doi:10.1007/978-3-319-14331-6_26 fatcat:vkka3mn4sbdyzcdlctgfaan5v4

Multi-Label Zero-Shot Learning with Transfer-Aware Label Embedding Projection [article]

Meng Ye, Yuhong Guo
2018 arXiv   pre-print
In this paper we propose a transfer-aware embedding projection approach to tackle multi-label zero-shot learning.  ...  Zero-shot learning transfers knowledge from seen classes to novel unseen classes to reduce human labor of labelling data for building new classifiers.  ...  These semantic embedding vectors have the nice property of catching general similarities between any pair of label phrases/words, but may not be optimal for multi-label classification and information transfer  ... 
arXiv:1808.02474v1 fatcat:dov2w7ofbvdg3kdfiprkb5sm3i

Transductive Multi-class and Multi-label Zero-shot Learning [article]

Yanwei Fu, Yongxin Yang, Timothy M. Hospedales, Tao Xiang, Shaogang Gong
2015 arXiv   pre-print
, and is used to bridge between these domains for knowledge transfer.  ...  In this paper we discuss two related lines of work improving the conventional approach: exploiting transductive learning ZSL, and generalising ZSL to the multi-label case.  ...  We propose a novel framework for multi-label zero-shot learning [9] .  ... 
arXiv:1503.07884v1 fatcat:or4zahtjj5atpoxks5n4dilega

Graphonomy: Universal Human Parsing via Graph Transfer Learning [article]

Ke Gong, Yiming Gao, Xiaodan Liang, Xiaohui Shen, Meng Wang, Liang Lin
2019 arXiv   pre-print
This poses many fundamental learning challenges, e.g. discovering underlying semantic structures among different label granularity, performing proper transfer learning across different image domains, and  ...  In particular, Graphonomy first learns and propagates compact high-level graph representation among the labels within one dataset via Intra-Graph Reasoning, and then transfers semantic information across  ...  similarity and semantic similarity is more reliable for information transferring.  ... 
arXiv:1904.04536v1 fatcat:di2yce3ytbhadml5lljt7yn66m

Graphonomy: Universal Human Parsing via Graph Transfer Learning

Ke Gong, Yiming Gao, Xiaodan Liang, Xiaohui Shen, Meng Wang, Liang Lin
2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
This poses many fundamental learning challenges, e.g. discovering underlying semantic structures among different label granularity, performing proper transfer learning across different image domains, and  ...  In particular, Graphonomy first learns and propagates compact high-level graph representation among the labels within one dataset via Intra-Graph Reasoning, and then transfers semantic information across  ...  similarity and semantic similarity is more reliable for information transferring.  ... 
doi:10.1109/cvpr.2019.00763 dblp:conf/cvpr/Gong0LS0L19 fatcat:rv5xgqag4jcghmzffi3s67fp2u

Label Efficient Learning of Transferable Representations across Domains and Tasks [article]

Zelun Luo, Yuliang Zou, Judy Hoffman, Li Fei-Fei
2017 arXiv   pre-print
We propose a framework that learns a representation transferable across different domains and tasks in a label efficient manner.  ...  In addition, we demonstrate the effectiveness of our framework on the transfer learning task from image object recognition to video action recognition.  ...  Next, we specially thank De-An Huang, Kenji Hata, Serena Yeung, Ozan Sener and all the members of Stanford Vision and Learning Lab for their insightful discussion and feedback.  ... 
arXiv:1712.00123v1 fatcat:r6f2zfpwtbf7vewux4m7rrs4ai

Class label autoencoder for zero-shot learning [article]

Guangfeng Lin, Caixia Fan, Wanjun Chen, Yajun Chen, Fan Zhao
2018 arXiv   pre-print
However, the projection function cannot be used between the feature space and multi-semantic embedding spaces, which have the diversity characteristic for describing the different semantic information  ...  CLA can not only build a uniform framework for adapting to multi-semantic embedding spaces, but also construct the encoder-decoder mechanism for constraining the bidirectional projection between the feature  ...  Yongqin Xian from MPI for Informatics, who provided the data source to us.  ... 
arXiv:1801.08301v1 fatcat:l6z42l2wsne5vk64jfzw76jez4

Graphonomy: Universal Image Parsing via Graph Reasoning and Transfer [article]

Liang Lin and Yiming Gao and Ke Gong and Meng Wang and Xiaodan Liang
2021 arXiv   pre-print
different domains for bidirectional knowledge transfer.  ...  This poses many fundamental learning challenges, e.g., discovering underlying semantic structures among different label granularity or mining label correlation across relevant tasks.  ...  similarity and semantic similarity is more reliable for information transferring.  ... 
arXiv:2101.10620v1 fatcat:hnbuqiugsfhvbc7phn5htmsvcy

Semantic image annotation using convolutional neural network and wordnet ontology

Jaison Saji Chacko, Tulasi B
2018 International Journal of Engineering & Technology  
This paper proposes an image annotation technique that uses deep learning and semantic labeling.  ...  Accurate annotation is critical for efficient image search and retrieval.  ...  As there is high probability for semantically similar concepts to co-occur in an image [13] , for example 'cars' and 'trucks'. The semantic similarity score can help in accurately labelling images.  ... 
doi:10.14419/ijet.v7i2.27.9886 fatcat:7ow6ocw22ng5xiydntzwnftvay

Transfer Knowledge between Cities

Ying Wei, Yu Zheng, Qiang Yang
2016 Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '16  
FLORAL learns semantically related dictionaries for multiple modalities from a source domain, and simultaneously transfers the dictionaries and labelled instances from the source into a target domain.  ...  In this paper, we propose a FLexible multimOdal tRAnsfer Learning (FLORAL) method to transfer knowledge from a city where there exist sufficient multimodal data and labels, to this kind of cities to fully  ...  Two strands of research, i.e., multi-task multi-view learning and multi-view transfer learning, enable knowledge transfer between domains with multimodal data.  ... 
doi:10.1145/2939672.2939830 dblp:conf/kdd/WeiZ016 fatcat:ax7lscs4jvgwraactqpi3pw7ci

Self-supervised asymmetric deep hashing with margin-scalable constraint [article]

Zhengyang Yu, Song Wu, Zhihao Dou, Erwin M.Bakker
2021 arXiv   pre-print
methods are based upon an oversimplified similarity assignment(i.e., 0 for instance pairs sharing no label, 1 for instance pairs sharing at least 1 label), 2) the exploration in multi-semantic relevance  ...  and precisely guides a feature learning network to preserve multilabel semantic information using an asymmetric learning strategy.  ...  Program for Chongqing Overseas Returnees (CX2018075).  ... 
arXiv:2012.03820v3 fatcat:fscm4ggdyrct3o6kso53mmriou

Transductive Zero-Shot Hashing for Multi-Label Image Retrieval [article]

Qin Zou, Zheng Zhang, Ling Cao, Long Chen, Song Wang
2019 arXiv   pre-print
Given semantic annotations such as class labels and pairwise similarities of the training data, hashing methods can learn and generate effective and compact binary codes.  ...  In this paper, for the first time, a novel transductive zero-shot hashing method is proposed for multi-label unseen image retrieval.  ...  the collective semantic representation for multi-label images.  ... 
arXiv:1911.07192v1 fatcat:mr5kdlsi5faxpn2a2obakha3se

Transfer Learning for Neural Semantic Parsing

Xing Fan, Emilio Monti, Lambert Mathias, Markus Dreyer
2017 Proceedings of the 2nd Workshop on Representation Learning for NLP  
In this paper, we propose using sequence-to-sequence in a multi-task setup for semantic parsing with a focus on transfer learning.  ...  Our experiments show that the multi-task setup aids transfer learning from an auxiliary task with large labeled data to a target task with smaller labeled data.  ...  Similar to this work, the authors use a neural semantic parsing model in a multi-task framework to jointly learn over multiple knowledge bases.  ... 
doi:10.18653/v1/w17-2607 dblp:conf/rep4nlp/FanMMD17 fatcat:p2cfqvaw6rearkc53xgzocfosq
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