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 $\chi^2$ 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