5,919 research outputs found
Zero-Shot Visual Recognition using Semantics-Preserving Adversarial Embedding Networks
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
Cold Dark Matter Isocurvature Perturbations: Cosmological Constraints and Applications
In this paper we present the constraints on cold dark matter (CDM)
isocurvature contributions to the cosmological perturbations. By employing
Markov Chain Monte Carlo method (MCMC), we perform a global analysis for
cosmological parameters using the latest astronomical data, such as 7-year
Wilkinson Microwave Anisotropy Probe (WMAP7) observations, matter power
spectrum from the Sloan Digital Sky Survey (SDSS) luminous red galaxies (LRG),
and "Union2" type Ia Supernovae (SNIa) sample. We find that the correlated
mixture of adiabatic and isocurvature modes are mildly better fitting to the
current data than the pure adiabatic ones, with the minimal given by
the likelihood analysis being reduced by 3.5. We also obtain a tight limit on
the fraction of the CDM isocurvature contributions, which should be less than
14.6% at 95% confidence level. With the presence of the isocurvature modes, the
adiabatic spectral index becomes slightly bigger, n_s^{\rm
adi}=0.972\pm0.014~(1\,\sigma), and the tilt for isocurvature spectrum could be
large, namely, the best fit value is n_s^{\rm iso}=3.020. Finally, we discuss
the effect on WMAP normalization priors, shift parameter R, acoustic scale l_A
and z_{*}, from the CDM isocurvaure perturbation. By fitting the mixed initial
condition to the combined data, we find the mean values of R, l_A and z_{*} can
be changed about 2.9\sigma, 2.8\sigma and 1.5\sigma respectively, comparing
with those obtained in the pure adiabatic condition.Comment: 9 pages, 5 figures, 3 tables, references adde
- …