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- W2896368184 abstract "Generalized zero-shot learning (GZSL) aims to recognize any test instance coming either from a known class or from a novel class that has no training instance. To synthesize training instances for novel classes and thus resolving GZSL as a common classification problem, we propose a Dissimilarity Representation Learning (DSS) method. Dissimilarity representation is to represent a specific instance in terms of its (dis)similarity to other instances in a visual or attribute based feature space. In the dissimilarity space, instances of the novel classes are synthesized by an end-to-end optimized neural network. The neural network realizes two-level feature mappings and domain adaptions in the dissimilarity space and the attribute based feature space. Experimental results on five benchmark datasets, i.e., AWA, AWA$_2$, SUN, CUB, and aPY, show that the proposed method improves the state-of-the-art with a large margin, approximately 10% gain in terms of the harmonic mean of the top-1 accuracy. Consequently, this paper establishes a new baseline for GZSL." @default.
- W2896368184 created "2018-10-26" @default.
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- W2896368184 date "2018-10-15" @default.
- W2896368184 modified "2023-09-25" @default.
- W2896368184 title "Dissimilarity Representation Learning for Generalized Zero-Shot Recognition" @default.
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- W2896368184 doi "https://doi.org/10.1145/3240508.3240686" @default.
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