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Domain Adaptative Causality Encoder
FarhadMoghimifar, GholamrezaHaffari, MahsaBaktashmotlagh....
Published date-11/27/2020
Current approaches which are mainly based on the extraction of low-level relations among individual events are limited by the shortage of publicly available labelled data. Therefore, the resulting models perform …
Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers
KaidiXu, huanzhang, ShiqiWang....
Published date-11/27/2020
Formal verification of neural networks (NNs) is a challenging and important problem. Existing efficient complete solvers typically require the branch-and-bound (BaB) process, which splits the problem domain into sub-domains and …
TStarBot-X: An Open-Sourced and Comprehensive Study for Efficient League Training in StarCraft II Full Game
LeiHan, JiechaoXiong, PengSun....
Published date-11/27/2020
ImitationLearning, Starcraft, StarcraftII
StarCraft, one of the most difficult esport games with long-standing history of professional tournaments, has attracted generations of players and fans, and also, intense attentions in artificial intelligence research. Recently, …
Enhancing Diversity in Teacher-Student Networks via Asymmetric branches for Unsupervised Person Re-identification
HaoChen, BenoitLagadec, FrancoisBremond....
Published date-11/27/2020
DomainAdaptation, PersonRe-Identification, UnsupervisedDomainAdaptation, UnsupervisedPersonRe-Identification
The objective of unsupervised person re-identification (Re-ID) is to learn discriminative features without labor-intensive identity annotations. State-of-the-art unsupervised Re-ID methods assign pseudo labels to unlabeled images in the target domain …
Navigating the GAN Parameter Space for Semantic Image Editing
AntonCherepkov, AndreyVoynov, ArtemBabenko....
Published date-11/27/2020
ImageRestoration, Image-to-ImageTranslation
Generative Adversarial Networks (GANs) are currently an indispensable tool for visual editing, being a standard component of image-to-image translation and image restoration pipelines. Furthermore, GANs are especially useful for controllable …
Active Learning in CNNs via Expected Improvement Maximization
UdaiG.Nagpal, DavidAKnowles....
Published date-11/27/2020
ActiveLearning
Deep learning models such as Convolutional Neural Networks (CNNs) have demonstrated high levels of effectiveness in a variety of domains, including computer vision and more recently, computational biology. However, training …