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20152022
most citedA Probabilistic Theory of Deep Learning

62 citations · 84 across the 6 of their papers we have counts for

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Showing cs.CVShow all

5 papers · 1 filter

cs.CV2022

Understanding robustness and generalization of artificial neural networks through Fourier masks

Nikos Karantzas, Emma Besier, Josue Ortega Caro +4

Despite the enormous success of artificial neural networks (ANNs) in many disciplines, the characterization of their computations and the origin of key properties such as generaliz…

cs.CV2020

Semi-Supervised StyleGAN for Disentanglement Learning

Weili Nie, Tero Karras, Animesh Garg +4

Disentanglement learning is crucial for obtaining disentangled representations and controllable generation. Current disentanglement methods face several inherent limitations: diffi…

cs.CV2018

A Bayesian Perspective of Convolutional Neural Networks through a Deconvolutional Generative Model

Tan Nguyen, Nhat Ho, Ankit Patel +3

Inspired by the success of Convolutional Neural Networks (CNNs) for supervised prediction in images, we design the Deconvolutional Generative Model (DGM), a new probabilistic gener…

cs.CV2018

Fast Retinomorphic Event Stream for Video Recognition and Reinforcement Learning

Wanjia Liu, Huaijin Chen, Rishab Goel +3

Good temporal representations are crucial for video understanding, and the state-of-the-art video recognition framework is based on two-stream networks. In such framework, besides…

cs.CV2018

A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations

Weili Nie, Yang Zhang, Ankit Patel

Backpropagation-based visualizations have been proposed to interpret convolutional neural networks (CNNs), however a theory is missing to justify their behaviors: Guided backpropag…