240 research outputs found
<ARTICLES>An Imperative Cultural Trend?: International Film Co-production Policy in Japan
In this article, I investigate the process of agenda building and implementation of the Japanese film policy which was concomitant with the Japanese cultural policy of ‘Cool Japan.’ This article is composed of three major analyses: 1) a review of the sociological literature and critiques on cultural industries and policy; 2) the discourse regarding the Cool Japan strategy and film policy in Japan; and 3) the incentive for international film co-production and the globalization of cultural policy in East Asia. First, this article explores the policymaking process associated with the Cool Japan strategy and Japanese film policy by analyzing the archival documents of the various governmental institutes in Japan. The Cool Japan policy has been built up as a national and cultural agenda by the government (an agenda-building effect) in the wake of globalization in East Asia. The promotion of the state of Japan is the main goal of this strategy, but the state does not always consider the current circumstances and problems of the Japanese film industry. The incentive for international film coproduction was created as the state was influenced by neighboring nations, even though the co-production strategy was initially exploited by a handful of directors who already had a track record of co-productions. I compare the cultural and film policies of South Korea and PRC with those of Japan by charting their interplay and examining their implications for global trends in the film industry. However, policy theory and discourse cannot be understood without also examining the industry situation itself. It is necessary to investigate the incentive provided for international film co-production in tandem with the Cool Japan strategy, in the name of which the government has committed several grave blunders. Thus, while the incentive for international film coproduction in Japan is policy-driven, at the same time it is also intertwined with media globalization in the competitive circumambience. Eventually, the question raised here must be: is it an imperative cultural trend or a commercial necessity for the Japanese film industry
GuidedMixup: An Efficient Mixup Strategy Guided by Saliency Maps
Data augmentation is now an essential part of the image training process, as
it effectively prevents overfitting and makes the model more robust against
noisy datasets. Recent mixing augmentation strategies have advanced to generate
the mixup mask that can enrich the saliency information, which is a supervisory
signal. However, these methods incur a significant computational burden to
optimize the mixup mask. From this motivation, we propose a novel
saliency-aware mixup method, GuidedMixup, which aims to retain the salient
regions in mixup images with low computational overhead. We develop an
efficient pairing algorithm that pursues to minimize the conflict of salient
regions of paired images and achieve rich saliency in mixup images. Moreover,
GuidedMixup controls the mixup ratio for each pixel to better preserve the
salient region by interpolating two paired images smoothly. The experiments on
several datasets demonstrate that GuidedMixup provides a good trade-off between
augmentation overhead and generalization performance on classification
datasets. In addition, our method shows good performance in experiments with
corrupted or reduced datasets.Comment: Published at AAAI2023 (Oral
Probabilistic Precision and Recall Towards Reliable Evaluation of Generative Models
Assessing the fidelity and diversity of the generative model is a difficult
but important issue for technological advancement. So, recent papers have
introduced k-Nearest Neighbor (NN) based precision-recall metrics to break
down the statistical distance into fidelity and diversity. While they provide
an intuitive method, we thoroughly analyze these metrics and identify
oversimplified assumptions and undesirable properties of kNN that result in
unreliable evaluation, such as susceptibility to outliers and insensitivity to
distributional changes. Thus, we propose novel metrics, P-precision and
P-recall (PP\&PR), based on a probabilistic approach that address the problems.
Through extensive investigations on toy experiments and state-of-the-art
generative models, we show that our PP\&PR provide more reliable estimates for
comparing fidelity and diversity than the existing metrics. The codes are
available at \url{https://github.com/kdst-team/Probablistic_precision_recall}
NaturalInversion: Data-Free Image Synthesis Improving Real-World Consistency
We introduce NaturalInversion, a novel model inversion-based method to
synthesize images that agrees well with the original data distribution without
using real data. In NaturalInversion, we propose: (1) a Feature Transfer
Pyramid which uses enhanced image prior of the original data by combining the
multi-scale feature maps extracted from the pre-trained classifier, (2) a
one-to-one approach generative model where only one batch of images are
synthesized by one generator to bring the non-linearity to optimization and to
ease the overall optimizing process, (3) learnable Adaptive Channel Scaling
parameters which are end-to-end trained to scale the output image channel to
utilize the original image prior further. With our NaturalInversion, we
synthesize images from classifiers trained on CIFAR-10/100 and show that our
images are more consistent with original data distribution than prior works by
visualization and additional analysis. Furthermore, our synthesized images
outperform prior works on various applications such as knowledge distillation
and pruning, demonstrating the effectiveness of our proposed method.Comment: Published at AAAI 202
Extreme coefficients in Geographically Weighted Regression and their effects on mapping
This study deals with the issue of extreme coefficients in geographically weighted regression (GWR) and their effects on mapping coefficients using three datasets with different spatial resolutions. We found that although GWR yields extreme coefficients regardless of the resolution of the dataset or types of kernel function, 1) the GWR tends to generate extreme coefficients for less spatially dense datasets, 2) coefficient maps based on polygon data representing aggregated areal units are more sensitive to extreme coefficients, and 3) coefficient maps using bandwidths generated by a fixed calibration procedure are more vulnerable to the extreme coefficients than adaptive calibration.extreme coefficient, fixed and adaptive calibrations, geographically weighted regression, Mapping, Research Methods/ Statistical Methods,
Meta-Learning with a Geometry-Adaptive Preconditioner
Model-agnostic meta-learning (MAML) is one of the most successful
meta-learning algorithms. It has a bi-level optimization structure where the
outer-loop process learns a shared initialization and the inner-loop process
optimizes task-specific weights. Although MAML relies on the standard gradient
descent in the inner-loop, recent studies have shown that controlling the
inner-loop's gradient descent with a meta-learned preconditioner can be
beneficial. Existing preconditioners, however, cannot simultaneously adapt in a
task-specific and path-dependent way. Additionally, they do not satisfy the
Riemannian metric condition, which can enable the steepest descent learning
with preconditioned gradient. In this study, we propose Geometry-Adaptive
Preconditioned gradient descent (GAP) that can overcome the limitations in
MAML; GAP can efficiently meta-learn a preconditioner that is dependent on
task-specific parameters, and its preconditioner can be shown to be a
Riemannian metric. Thanks to the two properties, the geometry-adaptive
preconditioner is effective for improving the inner-loop optimization.
Experiment results show that GAP outperforms the state-of-the-art MAML family
and preconditioned gradient descent-MAML (PGD-MAML) family in a variety of
few-shot learning tasks. Code is available at:
https://github.com/Suhyun777/CVPR23-GAP.Comment: Accepted at CVPR 2023. Code is available at:
https://github.com/Suhyun777/CVPR23-GAP; This is an extended version of our
previous CVPR23 wor
Inactivation of Medial Prefrontal Cortex Impairs Time Interval Discrimination in Rats
Several lines of evidence suggest the involvement of prefrontal cortex in time interval estimation. The underlying neural processes are poorly understood, however, in part because of the paucity of physiological studies. The goal of this study was to establish an interval timing task for physiological recordings in rats, and test the requirement of intact medial prefrontal cortex (mPFC) for performing the task. We established a temporal bisection procedure using six different time intervals ranging from 3018 to 4784 ms that needed to be discriminated as either long or short. Bilateral infusions of muscimol (GABAA receptor agonist) into the mPFC significantly impaired animal's performance in this task, even when the animals were required to discriminate between only the longest and shortest time intervals. These results show the requirement of intact mPFC in rats for time interval discrimination in the range of a few seconds
Parameter-Free Algorithms for Performative Regret Minimization under Decision-Dependent Distributions
This paper studies performative risk minimization, a formulation of
stochastic optimization under decision-dependent distributions. We consider the
general case where the performative risk can be non-convex, for which we
develop efficient parameter-free optimistic optimization-based methods. Our
algorithms significantly improve upon the existing Lipschitz bandit-based
method in many aspects. In particular, our framework does not require knowledge
about the sensitivity parameter of the distribution map and the Lipshitz
constant of the loss function. This makes our framework practically favorable,
together with the efficient optimistic optimization-based tree-search
mechanism. We provide experimental results that demonstrate the numerical
superiority of our algorithms over the existing method and other black-box
optimistic optimization methods
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