Influence Estimation for Generative Adversarial Networks

Identifying harmful instances, whose absence in a training dataset improves model performance, is important for building better machine learning models. Although previous studies have succeeded in estimating harmful instances under supervised settings, they cannot be trivially extended to generative adversarial networks (GANs). This is because previous approaches require that (1) the absence of a training instance directly affects the loss value and that (2) the change in the loss directly measures the harmfulness of the instance for the performance of a model. In GAN training, however, neither of the requirements is satisfied. This is because, (1) the generator's loss is not directly affected by the training instances as they are not part of the generator's training steps, and (2) the values of GAN's losses normally do not capture the generative performance of a model. To this end, (1) we propose an influence estimation method that uses the Jacobian of the gradient of the generator's loss with respect to the discriminator's parameters (and vice versa) to trace how the absence of an instance in the discriminator's training affects the generator's parameters, and (2) we propose a novel evaluation scheme, in which we assess harmfulness of each training instance on the basis of how GAN evaluation metric (e.g., inception score) is expect to change due to the removal of the instance. We experimentally verified that our influence estimation method correctly inferred the changes in GAN evaluation metrics. Further, we demonstrated that the removal of the identified harmful instances effectively improved the model's generative performance with respect to various GAN evaluation metrics.Comment: Published as a conference paper at ICLR 2021 (Spotlight

First multicenter survey on infectious keratitis following excimer laser surgery in Japan

AbstractPurposeTo report the first multicenter survey in Japan on infectious keratitis after excimer laser surgery.MethodsThe laser in situ keratomileusis (LASIK) Safety Network (LSN) Committee sent questionnaires to 28 LSN member hospitals to assess the total number of laser corneal surgeries, the number of infection cases (including suspicious cases), and the postoperative follow-up rate during a 3-year period.ResultsResponses were obtained from 27 (96.4%) of 28 institutions. One phototherapeutic keratectomy infection case was reported among 22,415 excimer laser surgery cases, which equates to an incidence rate of 0.004%. The follow-up rate was 94.14% (67.2–100%), 80.11% (41.0–96.1%), 57.95% (11.5–93.0%), and 46.64% (4.7–93.0%) at 1 month, 3 months, 6 months, and 12 months of follow-up, respectively.ConclusionInfectious keratitis is a potentially devastating complication of excimer laser surgery. We did not see any infectious keratitis for refractive cases. This first multicenter survey in Japan on infectious keratitis provides important information on the safety of this therapy

GCM Security Bounds Reconsidered

A constant of $2^{22}$ appears in the security bounds of the Galois/Counter Mode of Operation, GCM. In this paper, we first develop an algorithm to generate nonces that have a high counter-collision probability. We show concrete examples of nonces with the counter-collision probability of about $2^{20.75}/2^{128}$. This shows that the constant in the security bounds, $2^{22}$, cannot be made smaller than $2^{19.74}$ if the proof relies on ``the sum bound.\u27\u27 We next show that it is possible to avoid using the sum bound, leading to improved security bounds of GCM. One of our improvements shows that the constant of $2^{22}$ can be reduced to 32

Morphological changes in the meibomian glands of patients with phlyctenular keratitis: a multicenter cross-sectional study

Summary of 13 patients with phlyctenular keratitis. (XLSX 11 kb