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  • Open Access
    Authors: 
    Christian Spånslätt; Jinhong Park; Yuval Gefen; Alexander D. Mirlin;

    Electrical and thermal transport on a fractional quantum Hall edge are determined by topological quantities inherited from the corresponding bulk state. While electrical transport is the standard method for studying edges, thermal transport appears more challenging. Here, we show that the shot noise generated on the edge provides a fully electrical method to probe the edge structure. In the incoherent regime, the noise falls into three topologically distinct universality classes: charge transport is always ballistic while thermal transport is either ballistic, diffusive, or "antiballistic". Correspondingly, the noise either vanishes, decays algebraically or is constant up to exponentially small corrections in the edge length. Published version: 6+7 pages, 3+3 figures

  • Open Access
    Authors: 
    O. Teboul; Nir J. Shaviv;
    Publisher: Oxford University Press (OUP)

    ABSTRACT Linear polarization has been measured in several gamma-ray burst (GRB) afterglows. After a few days, polarization arises from the forward shock emission that depends on the post-shock magnetic field. The latter can originate both from compression of existing fields, here the interstellar medium (ISM) magnetic field, and from shock-generated instabilities. For short GRBs, previous modelling of the polarization arising from the forward shock considered a random field fully or partially confined to the shock plane. However, the ISM magnetic field likely consists of both random and ordered components. Here we study the impact of a more realistic magnetic field having both ordered and random components. We present our semi-analytical model and compute polarization curves arising for different magnetic field configurations. We find that the presence of an ordered component, even significantly weaker than the random one, has distinct signatures that could be detectable. In the presence of an ordered component not in the observer plane, we show that (i) for an observer inside the jet, the polarization angle θp either remains constant during all the afterglow phase or exhibits variations smaller than the 90° swing expected from a random component solely; (ii) for an off-axis observer, the polarization angle evolves from $\theta _\mathrm{ p}^{\max }$, before the jet break to its opposite after the jet break. We also find that the upper limit polarization for GRB 170817 requires a random field not fully confined to the shock plane and is compatible with an ordered component as large as half the random one.

  • Publication . Article . Preprint . 2019
    Open Access
    Authors: 
    Ben Ohayon; Joel Chocron; T. Hirsh; Ayala Glick-Magid; Yonatan Mishnayot; Ish Mukul; Hitesh Rahangdale; Sergei Vaintraub; Oded Heber; Doron Gazit; +1 more
    Publisher: Springer Science and Business Media LLC
    Project: EC | TRAPLAB (714118)

    We review the current status of the radioisotopes program at the Soreq Applied Research Accelerator Facility (SARAF), where we utilize an electrostatic-ion-beam trap and a magneto-optical trap for studying the nuclear $\beta$-decay from trapped radioactive atoms and ions. The differential energy spectra of $\beta$'s and recoil ions emerging from the decay is sensitive to beyond standard model interactions and is complementary to high energy searches. The completed facility SARAF-II will be one of the world's most powerful deuteron, proton and fast neutron sources, producing light radioactive isotopes in unprecedented amounts, needed for obtaining enough statistics for a high precision measurement.

  • Publication . Article . Preprint . 2021
    Open Access
    Authors: 
    Ó. Rodríguez; N Meza; J Pineda-García; M Ramirez;
    Publisher: Oxford University Press (OUP)
    Project: EC | EMERGE (833031)

    We present $^{56}$Ni mass estimates for 110 normal Type II supernovae (SNe II), computed here from their luminosity in the radioactive tail. This sample consists of SNe from the literature, with at least three photometric measurements in a single optical band within 95-320 d since explosion. To convert apparent magnitudes to bolometric ones, we compute bolometric corrections (BCs) using 15 SNe in our sample having optical and near-IR photometry, along with three sets of SN II atmosphere models to account for the unobserved flux. We find that the $I$- and $i$-band are best suited to estimate luminosities through the BC technique. The $^{56}$Ni mass distribution of our SN sample has a minimum and maximum of 0.005 and 0.177 M$_{\odot}$, respectively, and a selection-bias-corrected average of $0.037\pm0.005$ M$_{\odot}$. Using the latter value together with iron isotope ratios of two sets of core-collapse (CC) nucleosynthesis models, we calculate a mean iron yield of $0.040\pm0.005$ M$_{\odot}$ for normal SNe II. Combining this result with recent mean $^{56}$Ni mass measurements for other CC SN subtypes, we estimate a mean iron yield $$36 per cent. We also find that the empirical relation between $^{56}$Ni mass and steepness parameter ($S$) is poorly suited to measure the $^{56}$Ni mass of normal SNe II. Instead, we present a correlation between $^{56}$Ni mass, $S$, and absolute magnitude at 50 d since explosion. The latter allows to measure $^{56}$Ni masses of normal SNe II with a precision around 30 per cent. Comment: 33 pages, 20 figures, 6 figures in appendix, accepted for publication to MNRAS

  • Publication . Article . Preprint . 2020 . Embargo End Date: 01 Jan 2020
    Open Access
    Authors: 
    Mathov, Yael; Levy, Eden; Katzir, Ziv; Shabtai, Asaf; Elovici, Yuval;
    Publisher: arXiv

    Recent work on adversarial learning has focused mainly on neural networks and domains where those networks excel, such as computer vision, or audio processing. The data in these domains is typically homogeneous, whereas heterogeneous tabular datasets domains remain underexplored despite their prevalence. When searching for adversarial patterns within heterogeneous input spaces, an attacker must simultaneously preserve the complex domain-specific validity rules of the data, as well as the adversarial nature of the identified samples. As such, applying adversarial manipulations to heterogeneous datasets has proved to be a challenging task, and no generic attack method was suggested thus far. We, however, argue that machine learning models trained on heterogeneous tabular data are as susceptible to adversarial manipulations as those trained on continuous or homogeneous data such as images. To support our claim, we introduce a generic optimization framework for identifying adversarial perturbations in heterogeneous input spaces. We define distribution-aware constraints for preserving the consistency of the adversarial examples and incorporate them by embedding the heterogeneous input into a continuous latent space. Due to the nature of the underlying datasets We focus on $\ell_0$ perturbations, and demonstrate their applicability in real life. We demonstrate the effectiveness of our approach using three datasets from different content domains. Our results demonstrate that despite the constraints imposed on input validity in heterogeneous datasets, machine learning models trained using such data are still equally susceptible to adversarial examples.

  • Publication . Preprint . Conference object . Article . 2019 . Embargo End Date: 01 Jan 2019
    Open Access
    Authors: 
    Yichao Yan; Qiang Zhang; Bingbing Ni; Wendong Zhang; Minghao Xu; Xiaokang Yang;
    Publisher: arXiv

    Person re-identification has achieved great progress with deep convolutional neural networks. However, most previous methods focus on learning individual appearance feature embedding, and it is hard for the models to handle difficult situations with different illumination, large pose variance and occlusion. In this work, we take a step further and consider employing context information for person search. For a probe-gallery pair, we first propose a contextual instance expansion module, which employs a relative attention module to search and filter useful context information in the scene. We also build a graph learning framework to effectively employ context pairs to update target similarity. These two modules are built on top of a joint detection and instance feature learning framework, which improves the discriminativeness of the learned features. The proposed framework achieves state-of-the-art performance on two widely used person search datasets. Comment: To appear in CVPR 2019

  • Publication . Article . Preprint . 2020 . Embargo End Date: 01 Jan 2006
    Open Access
    Authors: 
    Adi Shafir; David Andelman;
    Publisher: arXiv

    We study the contribution of polyelectrolytes in solution to the bending moduli of charged membranes. Using the Helfrich free energy, and within the mean-field theory, we calculate the dependence of the bending moduli on the electrostatics and short-range interactions between the membrane and the polyelectrolyte chains. The most significant effect is seen for strong short-range interactions and low amounts of added salt where a substantial increase in the bending moduli of order $1 k_BT$ is obtained. From short-range repulsive membranes, the polyelectrolyte contribution to the bending moduli is small, of order $0.1 k_BT$ up to at most $1 k_BT$. For weak short-range attraction, the increase in membrane rigidity is smaller and of less significance. It may even become negative for large enough amounts of added salt. Our numerical results are obtained by solving the adsorption problem in spherical and cylindrical geometries. In some cases the bending moduli are shown to follow simple scaling laws. Comment: 16 pages, 6 figures

  • Publication . Article . Preprint . 2021
    Open Access English
    Authors: 
    Evgeni Grishin; Alexey Bobrick; Ryosuke Hirai; Ilya Mandel; Hagai B. Perets;
    Project: ARC | ARC Future Fellowships - ... (FT190100574), EC | SNeX (865932)

    Active galactic nuclei (AGN) are prominent environments for stellar capture, growth and formation. These environments may catalyze stellar mergers and explosive transients, such as thermonuclear and core-collapse supernovae (SNe). SN explosions in AGN discs generate strong shocks, leading to unique observable signatures. We develop an analytical model which follows the evolution of the shock propagating in the disc until it eventually breaks out. We derive the peak luminosity, bolometric lightcurve, and breakout time. The peak luminosities may exceed $10^{45}$ erg s$^{-1}$ and last from hours to days. The brightest explosions occur in regions of reduced density; either off-plane, or in discs around low-mass central black holes ($\sim 10^6\ M_\odot$), or in starved subluminous AGNs. Explosions in the latter two sites are easier to observe due to a reduced AGN background luminosity. We perform suites of 1D Lagrangian radiative hydrodynamics SNEC code simulations to validate our results and obtain the luminosity in different bands, and 2D axisymmetric Eulerian hydrodynamics code HORMONE simulations to study the morphology of the ejecta and its deviation from spherical symmetry. The observed signature is expected to be a bright blue, UV, or X-ray flare on top of the AGN luminosity from the initial shock breakout, while the subsequent red part of the lightcurve will largely be unobservable. We estimate the upper limit for the total event rate to be $\mathcal{R}\lesssim 100\ \rm yr^{-1}\ Gpc^{-3}$ for optimal conditions and discuss the large uncertainties in this estimate. Future high-cadence transient searches may reveal these events. Some existing tidal disruption event candidates may originate from AGN supernovae. Accepted to MNRAS

  • Publication . Preprint . Conference object . Article . 2019
    Open Access
    Authors: 
    Michael Elkin; Arnold Filtser; Ofer Neiman;
    Publisher: ACM

    A $t$-{\em spanner} $H$ of a weighted graph $G=(V,E,w)$ is a subgraph that approximates all pairwise distances up to a factor of $t$. The {\em lightness} of $H$ is defined as the ratio between the weight of $H$ to that of the minimum spanning tree. An $(\alpha,\beta)$-{\em Shallow Light Tree} (SLT) is a tree of lightness $\beta$, that approximates all distances from a designated root vertex up to a factor of $\alpha$. A long line of works resulted in efficient algorithms that produce (nearly) optimal light spanners and SLTs. Some of the most notable algorithmic applications of light spanners and SLTs are in distributed settings. Surprisingly, so far there are no known efficient distributed algorithms for constructing these objects in general graphs. In this paper we devise efficient distributed algorithms in the CONGEST model for constructing light spanners and SLTs, with near optimal parameters. Specifically, for any $k\ge 1$ and $01$ we provide an $(\alpha,1+\frac{O(1)}{\alpha-1})$-SLT in $(\sqrt{n}+D)\cdot n^{o(1)}$ rounds. The running time of our algorithms cannot be substantially improved. We also consider spanners for the family of doubling graphs, and devise a $(\sqrt{n}+D)\cdot n^{o(1)}$ rounds algorithm in the CONGEST model that computes a $(1+\epsilon)$-spanner with lightness $(\log n)/\epsilon^{O(1)}$. As a stepping stone, which is interesting in its own right, we first develop a distributed algorithm for constructing nets (for arbitrary weighted graphs), generalizing previous algorithms that worked only for unweighted graphs.

  • Publication . Preprint . Article . 2021 . Embargo End Date: 01 Jan 2020
    Open Access
    Authors: 
    Yaniv Benny; Tomer Galanti; Sagie Benaim; Lior Wolf;
    Publisher: arXiv
    Project: EC | DeepFace (725974)

    We present two new metrics for evaluating generative models in the class-conditional image generation setting. These metrics are obtained by generalizing the two most popular unconditional metrics: the Inception Score (IS) and the Fre'chet Inception Distance (FID). A theoretical analysis shows the motivation behind each proposed metric and links the novel metrics to their unconditional counterparts. The link takes the form of a product in the case of IS or an upper bound in the FID case. We provide an extensive empirical evaluation, comparing the metrics to their unconditional variants and to other metrics, and utilize them to analyze existing generative models, thus providing additional insights about their performance, from unlearned classes to mode collapse. Comment: To be published in "INTERNATIONAL JOURNAL OF COMPUTER VISION"

Advanced search in
Research products
arrow_drop_down
Searching FieldsTerms
Any field
arrow_drop_down
includes
arrow_drop_down
Include:
11,165 Research products, page 1 of 1,117
  • Open Access
    Authors: 
    Christian Spånslätt; Jinhong Park; Yuval Gefen; Alexander D. Mirlin;

    Electrical and thermal transport on a fractional quantum Hall edge are determined by topological quantities inherited from the corresponding bulk state. While electrical transport is the standard method for studying edges, thermal transport appears more challenging. Here, we show that the shot noise generated on the edge provides a fully electrical method to probe the edge structure. In the incoherent regime, the noise falls into three topologically distinct universality classes: charge transport is always ballistic while thermal transport is either ballistic, diffusive, or "antiballistic". Correspondingly, the noise either vanishes, decays algebraically or is constant up to exponentially small corrections in the edge length. Published version: 6+7 pages, 3+3 figures

  • Open Access
    Authors: 
    O. Teboul; Nir J. Shaviv;
    Publisher: Oxford University Press (OUP)

    ABSTRACT Linear polarization has been measured in several gamma-ray burst (GRB) afterglows. After a few days, polarization arises from the forward shock emission that depends on the post-shock magnetic field. The latter can originate both from compression of existing fields, here the interstellar medium (ISM) magnetic field, and from shock-generated instabilities. For short GRBs, previous modelling of the polarization arising from the forward shock considered a random field fully or partially confined to the shock plane. However, the ISM magnetic field likely consists of both random and ordered components. Here we study the impact of a more realistic magnetic field having both ordered and random components. We present our semi-analytical model and compute polarization curves arising for different magnetic field configurations. We find that the presence of an ordered component, even significantly weaker than the random one, has distinct signatures that could be detectable. In the presence of an ordered component not in the observer plane, we show that (i) for an observer inside the jet, the polarization angle θp either remains constant during all the afterglow phase or exhibits variations smaller than the 90° swing expected from a random component solely; (ii) for an off-axis observer, the polarization angle evolves from $\theta _\mathrm{ p}^{\max }$, before the jet break to its opposite after the jet break. We also find that the upper limit polarization for GRB 170817 requires a random field not fully confined to the shock plane and is compatible with an ordered component as large as half the random one.

  • Publication . Article . Preprint . 2019
    Open Access
    Authors: 
    Ben Ohayon; Joel Chocron; T. Hirsh; Ayala Glick-Magid; Yonatan Mishnayot; Ish Mukul; Hitesh Rahangdale; Sergei Vaintraub; Oded Heber; Doron Gazit; +1 more
    Publisher: Springer Science and Business Media LLC
    Project: EC | TRAPLAB (714118)

    We review the current status of the radioisotopes program at the Soreq Applied Research Accelerator Facility (SARAF), where we utilize an electrostatic-ion-beam trap and a magneto-optical trap for studying the nuclear $\beta$-decay from trapped radioactive atoms and ions. The differential energy spectra of $\beta$'s and recoil ions emerging from the decay is sensitive to beyond standard model interactions and is complementary to high energy searches. The completed facility SARAF-II will be one of the world's most powerful deuteron, proton and fast neutron sources, producing light radioactive isotopes in unprecedented amounts, needed for obtaining enough statistics for a high precision measurement.

  • Publication . Article . Preprint . 2021
    Open Access
    Authors: 
    Ó. Rodríguez; N Meza; J Pineda-García; M Ramirez;
    Publisher: Oxford University Press (OUP)
    Project: EC | EMERGE (833031)

    We present $^{56}$Ni mass estimates for 110 normal Type II supernovae (SNe II), computed here from their luminosity in the radioactive tail. This sample consists of SNe from the literature, with at least three photometric measurements in a single optical band within 95-320 d since explosion. To convert apparent magnitudes to bolometric ones, we compute bolometric corrections (BCs) using 15 SNe in our sample having optical and near-IR photometry, along with three sets of SN II atmosphere models to account for the unobserved flux. We find that the $I$- and $i$-band are best suited to estimate luminosities through the BC technique. The $^{56}$Ni mass distribution of our SN sample has a minimum and maximum of 0.005 and 0.177 M$_{\odot}$, respectively, and a selection-bias-corrected average of $0.037\pm0.005$ M$_{\odot}$. Using the latter value together with iron isotope ratios of two sets of core-collapse (CC) nucleosynthesis models, we calculate a mean iron yield of $0.040\pm0.005$ M$_{\odot}$ for normal SNe II. Combining this result with recent mean $^{56}$Ni mass measurements for other CC SN subtypes, we estimate a mean iron yield $$36 per cent. We also find that the empirical relation between $^{56}$Ni mass and steepness parameter ($S$) is poorly suited to measure the $^{56}$Ni mass of normal SNe II. Instead, we present a correlation between $^{56}$Ni mass, $S$, and absolute magnitude at 50 d since explosion. The latter allows to measure $^{56}$Ni masses of normal SNe II with a precision around 30 per cent. Comment: 33 pages, 20 figures, 6 figures in appendix, accepted for publication to MNRAS

  • Publication . Article . Preprint . 2020 . Embargo End Date: 01 Jan 2020
    Open Access
    Authors: 
    Mathov, Yael; Levy, Eden; Katzir, Ziv; Shabtai, Asaf; Elovici, Yuval;
    Publisher: arXiv

    Recent work on adversarial learning has focused mainly on neural networks and domains where those networks excel, such as computer vision, or audio processing. The data in these domains is typically homogeneous, whereas heterogeneous tabular datasets domains remain underexplored despite their prevalence. When searching for adversarial patterns within heterogeneous input spaces, an attacker must simultaneously preserve the complex domain-specific validity rules of the data, as well as the adversarial nature of the identified samples. As such, applying adversarial manipulations to heterogeneous datasets has proved to be a challenging task, and no generic attack method was suggested thus far. We, however, argue that machine learning models trained on heterogeneous tabular data are as susceptible to adversarial manipulations as those trained on continuous or homogeneous data such as images. To support our claim, we introduce a generic optimization framework for identifying adversarial perturbations in heterogeneous input spaces. We define distribution-aware constraints for preserving the consistency of the adversarial examples and incorporate them by embedding the heterogeneous input into a continuous latent space. Due to the nature of the underlying datasets We focus on $\ell_0$ perturbations, and demonstrate their applicability in real life. We demonstrate the effectiveness of our approach using three datasets from different content domains. Our results demonstrate that despite the constraints imposed on input validity in heterogeneous datasets, machine learning models trained using such data are still equally susceptible to adversarial examples.

  • Publication . Preprint . Conference object . Article . 2019 . Embargo End Date: 01 Jan 2019
    Open Access
    Authors: 
    Yichao Yan; Qiang Zhang; Bingbing Ni; Wendong Zhang; Minghao Xu; Xiaokang Yang;
    Publisher: arXiv

    Person re-identification has achieved great progress with deep convolutional neural networks. However, most previous methods focus on learning individual appearance feature embedding, and it is hard for the models to handle difficult situations with different illumination, large pose variance and occlusion. In this work, we take a step further and consider employing context information for person search. For a probe-gallery pair, we first propose a contextual instance expansion module, which employs a relative attention module to search and filter useful context information in the scene. We also build a graph learning framework to effectively employ context pairs to update target similarity. These two modules are built on top of a joint detection and instance feature learning framework, which improves the discriminativeness of the learned features. The proposed framework achieves state-of-the-art performance on two widely used person search datasets. Comment: To appear in CVPR 2019

  • Publication . Article . Preprint . 2020 . Embargo End Date: 01 Jan 2006
    Open Access
    Authors: 
    Adi Shafir; David Andelman;
    Publisher: arXiv

    We study the contribution of polyelectrolytes in solution to the bending moduli of charged membranes. Using the Helfrich free energy, and within the mean-field theory, we calculate the dependence of the bending moduli on the electrostatics and short-range interactions between the membrane and the polyelectrolyte chains. The most significant effect is seen for strong short-range interactions and low amounts of added salt where a substantial increase in the bending moduli of order $1 k_BT$ is obtained. From short-range repulsive membranes, the polyelectrolyte contribution to the bending moduli is small, of order $0.1 k_BT$ up to at most $1 k_BT$. For weak short-range attraction, the increase in membrane rigidity is smaller and of less significance. It may even become negative for large enough amounts of added salt. Our numerical results are obtained by solving the adsorption problem in spherical and cylindrical geometries. In some cases the bending moduli are shown to follow simple scaling laws. Comment: 16 pages, 6 figures

  • Publication . Article . Preprint . 2021
    Open Access English
    Authors: 
    Evgeni Grishin; Alexey Bobrick; Ryosuke Hirai; Ilya Mandel; Hagai B. Perets;
    Project: ARC | ARC Future Fellowships - ... (FT190100574), EC | SNeX (865932)

    Active galactic nuclei (AGN) are prominent environments for stellar capture, growth and formation. These environments may catalyze stellar mergers and explosive transients, such as thermonuclear and core-collapse supernovae (SNe). SN explosions in AGN discs generate strong shocks, leading to unique observable signatures. We develop an analytical model which follows the evolution of the shock propagating in the disc until it eventually breaks out. We derive the peak luminosity, bolometric lightcurve, and breakout time. The peak luminosities may exceed $10^{45}$ erg s$^{-1}$ and last from hours to days. The brightest explosions occur in regions of reduced density; either off-plane, or in discs around low-mass central black holes ($\sim 10^6\ M_\odot$), or in starved subluminous AGNs. Explosions in the latter two sites are easier to observe due to a reduced AGN background luminosity. We perform suites of 1D Lagrangian radiative hydrodynamics SNEC code simulations to validate our results and obtain the luminosity in different bands, and 2D axisymmetric Eulerian hydrodynamics code HORMONE simulations to study the morphology of the ejecta and its deviation from spherical symmetry. The observed signature is expected to be a bright blue, UV, or X-ray flare on top of the AGN luminosity from the initial shock breakout, while the subsequent red part of the lightcurve will largely be unobservable. We estimate the upper limit for the total event rate to be $\mathcal{R}\lesssim 100\ \rm yr^{-1}\ Gpc^{-3}$ for optimal conditions and discuss the large uncertainties in this estimate. Future high-cadence transient searches may reveal these events. Some existing tidal disruption event candidates may originate from AGN supernovae. Accepted to MNRAS

  • Publication . Preprint . Conference object . Article . 2019
    Open Access
    Authors: 
    Michael Elkin; Arnold Filtser; Ofer Neiman;
    Publisher: ACM

    A $t$-{\em spanner} $H$ of a weighted graph $G=(V,E,w)$ is a subgraph that approximates all pairwise distances up to a factor of $t$. The {\em lightness} of $H$ is defined as the ratio between the weight of $H$ to that of the minimum spanning tree. An $(\alpha,\beta)$-{\em Shallow Light Tree} (SLT) is a tree of lightness $\beta$, that approximates all distances from a designated root vertex up to a factor of $\alpha$. A long line of works resulted in efficient algorithms that produce (nearly) optimal light spanners and SLTs. Some of the most notable algorithmic applications of light spanners and SLTs are in distributed settings. Surprisingly, so far there are no known efficient distributed algorithms for constructing these objects in general graphs. In this paper we devise efficient distributed algorithms in the CONGEST model for constructing light spanners and SLTs, with near optimal parameters. Specifically, for any $k\ge 1$ and $01$ we provide an $(\alpha,1+\frac{O(1)}{\alpha-1})$-SLT in $(\sqrt{n}+D)\cdot n^{o(1)}$ rounds. The running time of our algorithms cannot be substantially improved. We also consider spanners for the family of doubling graphs, and devise a $(\sqrt{n}+D)\cdot n^{o(1)}$ rounds algorithm in the CONGEST model that computes a $(1+\epsilon)$-spanner with lightness $(\log n)/\epsilon^{O(1)}$. As a stepping stone, which is interesting in its own right, we first develop a distributed algorithm for constructing nets (for arbitrary weighted graphs), generalizing previous algorithms that worked only for unweighted graphs.

  • Publication . Preprint . Article . 2021 . Embargo End Date: 01 Jan 2020
    Open Access
    Authors: 
    Yaniv Benny; Tomer Galanti; Sagie Benaim; Lior Wolf;
    Publisher: arXiv
    Project: EC | DeepFace (725974)

    We present two new metrics for evaluating generative models in the class-conditional image generation setting. These metrics are obtained by generalizing the two most popular unconditional metrics: the Inception Score (IS) and the Fre'chet Inception Distance (FID). A theoretical analysis shows the motivation behind each proposed metric and links the novel metrics to their unconditional counterparts. The link takes the form of a product in the case of IS or an upper bound in the FID case. We provide an extensive empirical evaluation, comparing the metrics to their unconditional variants and to other metrics, and utilize them to analyze existing generative models, thus providing additional insights about their performance, from unlearned classes to mode collapse. Comment: To be published in "INTERNATIONAL JOURNAL OF COMPUTER VISION"

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