The aim of the article is to analyze the evolution of a radical left group in France that created a scission inside the Fourth International after World War II. The group founded a review Socialisme ou Barbarie that c...The aim of the article is to analyze the evolution of a radical left group in France that created a scission inside the Fourth International after World War II. The group founded a review Socialisme ou Barbarie that criticized Marxism and the Trotskyist interpretation of the status of the USSR. The rigorous description of this review reveals the mixture of strong theoretical views on bureaucratic societies and empirical investigations of reactions against those societies. The hypothesis is that this group failed to be a new political force. As a matter of fact, is it possible to depict the evolution of Socialisme ou Barbarie as an investigative journalism based on a strong political and philosophical theory？展开更多
In some image classification tasks, similarities among different categories are different and the samples are usually misclassified as highly similar categories. To distinguish highly similar categories, more specific...In some image classification tasks, similarities among different categories are different and the samples are usually misclassified as highly similar categories. To distinguish highly similar categories, more specific features are required so that the classifier can improve the classification performance. In this paper, we propose a novel two-level hierarchical feature learning framework based on the deep convolutional neural network(CNN), which is simple and effective. First, the deep feature extractors of different levels are trained using the transfer learning method that fine-tunes the pre-trained deep CNN model toward the new target dataset. Second, the general feature extracted from all the categories and the specific feature extracted from highly similar categories are fused into a feature vector. Then the final feature representation is fed into a linear classifier. Finally, experiments using the Caltech-256, Oxford Flower-102, and Tasmania Coral Point Count(CPC) datasets demonstrate that the expression ability of the deep features resulting from two-level hierarchical feature learning is powerful. Our proposed method effectively increases the classification accuracy in comparison with flat multiple classification methods.展开更多
文摘The aim of the article is to analyze the evolution of a radical left group in France that created a scission inside the Fourth International after World War II. The group founded a review Socialisme ou Barbarie that criticized Marxism and the Trotskyist interpretation of the status of the USSR. The rigorous description of this review reveals the mixture of strong theoretical views on bureaucratic societies and empirical investigations of reactions against those societies. The hypothesis is that this group failed to be a new political force. As a matter of fact, is it possible to depict the evolution of Socialisme ou Barbarie as an investigative journalism based on a strong political and philosophical theory？
基金Project supported by the National Natural Science Foundation of China(No.61379074)the Zhejiang Provincial Natural Science Foundation of China(Nos.LZ12F02003 and LY15F020035)
文摘In some image classification tasks, similarities among different categories are different and the samples are usually misclassified as highly similar categories. To distinguish highly similar categories, more specific features are required so that the classifier can improve the classification performance. In this paper, we propose a novel two-level hierarchical feature learning framework based on the deep convolutional neural network(CNN), which is simple and effective. First, the deep feature extractors of different levels are trained using the transfer learning method that fine-tunes the pre-trained deep CNN model toward the new target dataset. Second, the general feature extracted from all the categories and the specific feature extracted from highly similar categories are fused into a feature vector. Then the final feature representation is fed into a linear classifier. Finally, experiments using the Caltech-256, Oxford Flower-102, and Tasmania Coral Point Count(CPC) datasets demonstrate that the expression ability of the deep features resulting from two-level hierarchical feature learning is powerful. Our proposed method effectively increases the classification accuracy in comparison with flat multiple classification methods.
