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Move to See Better: Towards Self-Supervised Amodal Object Detection
ZhaoyuanFang, AyushJain, GabrielSarch....
Published date-11/30/2020
ObjectDetection
Humans learn to better understand the world by moving around their environment to get more informative viewpoints of the scene. Most methods for 2D visual recognition tasks such as object …
Automating Artifact Detection in Video Games
ParmidaDavarmanesh, KuanhaoJiang, TingtingOu....
Published date-11/30/2020
In spite of advances in gaming hardware and software, gameplay is often tainted with graphics errors, glitches, and screen artifacts. This proof of concept study presents a machine learning approach …
FCM-RDpA: TSK Fuzzy Regression Model Construction Using Fuzzy C-Means Clustering, Regularization, DropRule, and Powerball AdaBelief
ZhenhuaShi, DongruiWu, ChenfengGuo....
Published date-11/30/2020
Clustering
To effectively optimize Takagi-Sugeno-Kang (TSK) fuzzy systems for regression problems, a mini-batch gradient descent with regularization, DropRule, and AdaBound (MBGD-RDA) algorithm was recently proposed. This paper further proposes FCM-RDpA, which …
Machine Translation of Novels in the Age of Transformer
AntonioToral, AntoniOliver, PauRibasBallestín....
Published date-11/30/2020
MachineTranslation
In this chapter we build a machine translation (MT) system tailored to the literary domain, specifically to novels, based on the state-of-the-art architecture in neural MT (NMT), the Transformer (Vaswani …
DUT: Learning Video Stabilization by Simply Watching Unstable Videos
YufeiXu, JingZhang, StephenJ.Maybank....
Published date-11/30/2020
We propose a Deep Unsupervised Trajectory-based stabilization framework (DUT) in this paper. Traditional stabilizers focus on trajectory-based smoothing, which is controllable but fragile in occluded and textureless cases regarding the …
KD-Lib: A PyTorch library for Knowledge Distillation, Pruning and Quantization
HetShah, AvishreeKhare, NeelayShah....
Published date-11/30/2020
ModelCompression, Quantization
In recent years, the growing size of neural networks has led to a vast amount of research concerning compression techniques to mitigate the drawbacks of such large sizes. Most of …