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• W2895980628 abstract "Majority of the current dimensionality reduction or retrieval techniques rely on embedding the learned feature representations onto a computable metric space. Once the learned features are mapped, a distance metric aids the bridging of gaps between similar instances. Since the scaled projection is not exploited in these methods, discriminative embedding onto a hyperspace becomes a challenge. In this paper, we propose to inwardly scale feature representations in proportional to projecting them onto a hypersphere manifold for discriminative analysis. We further propose a novel, yet simpler, convolutional neural network based architecture and extensively evaluate the proposed methodology in the context of classification and retrieval tasks obtaining results comparable to state-of-the-art techniques." @default.
• W2895980628 created "2018-10-26" @default.
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• W2895980628 date "2018-10-16" @default.
• W2895980628 modified "2023-09-27" @default.
• W2895980628 title "Learning Inward Scaled Hypersphere Embedding: Exploring Projections in Higher Dimensions." @default.
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• W2895980628 hasPublicationYear "2018" @default.
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