3,549 research outputs found

    Zero-Shot Visual Recognition using Semantics-Preserving Adversarial Embedding Networks

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    We propose a novel framework called Semantics-Preserving Adversarial Embedding Network (SP-AEN) for zero-shot visual recognition (ZSL), where test images and their classes are both unseen during training. SP-AEN aims to tackle the inherent problem --- semantic loss --- in the prevailing family of embedding-based ZSL, where some semantics would be discarded during training if they are non-discriminative for training classes, but could become critical for recognizing test classes. Specifically, SP-AEN prevents the semantic loss by introducing an independent visual-to-semantic space embedder which disentangles the semantic space into two subspaces for the two arguably conflicting objectives: classification and reconstruction. Through adversarial learning of the two subspaces, SP-AEN can transfer the semantics from the reconstructive subspace to the discriminative one, accomplishing the improved zero-shot recognition of unseen classes. Comparing with prior works, SP-AEN can not only improve classification but also generate photo-realistic images, demonstrating the effectiveness of semantic preservation. On four popular benchmarks: CUB, AWA, SUN and aPY, SP-AEN considerably outperforms other state-of-the-art methods by an absolute performance difference of 12.2\%, 9.3\%, 4.0\%, and 3.6\% in terms of harmonic mean value

    Thermally-Formed Oxide on Magnesium and Magnesium Alloys

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    Fatigue Deformation Mechanisms of Zirconia Ceramics

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    Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65237/1/j.1151-2916.1992.tb05558.x.pd

    Fatigue of Yttria-Stabilized Zirconia: I, Fatigue Damage, Fracture Origins, and Lifetime Prediction

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    Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65792/1/j.1151-2916.1991.tb04088.x.pd
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