Rolling Shutter Correction
90 papers with code • 0 benchmarks • 0 datasets
Rolling Shutter Correction
Benchmarks
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Most implemented papers
Deformable Part Models are Convolutional Neural Networks
Deformable part models (DPMs) and convolutional neural networks (CNNs) are two widely used tools for visual recognition.
Generating images with recurrent adversarial networks
Gatys et al. (2015) showed that optimizing pixels to match features in a convolutional network with respect reference image features is a way to render images of high visual quality.
A Deep Primal-Dual Network for Guided Depth Super-Resolution
In this paper we present a novel method to increase the spatial resolution of depth images.
Variational Generative Stochastic Networks with Collaborative Shaping
We develop an approach to training generative models based on unrolling a variational auto-encoder into a Markov chain, and shaping the chain's trajectories using a technique inspired by recent work in Approximate Bayesian computation.
Frank-Wolfe Network: An Interpretable Deep Structure for Non-Sparse Coding
We propose an interpretable deep structure namely Frank-Wolfe Network (F-W Net), whose architecture is inspired by unrolling and truncating the Frank-Wolfe algorithm for solving an $L_p$-norm constrained problem with $p\geq 1$.
End-to-End Video Captioning with Multitask Reinforcement Learning
adwardlee/multitask-end-to-end-video-captioning
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Although end-to-end (E2E) learning has led to impressive progress on a variety of visual understanding tasks, it is often impeded by hardware constraints (e. g., GPU memory) and is prone to overfitting.
Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling
wushanshan/L1AE
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Our experiments show that there is indeed additional structure beyond sparsity in the real datasets; our method is able to discover it and exploit it to create excellent reconstructions with fewer measurements (by a factor of 1. 1-3x) compared to the previous state-of-the-art methods.
Continual Learning of Recurrent Neural Networks by Locally Aligning Distributed Representations
We compare our model and learning procedure to other back-propagation through time alternatives (which also tend to be computationally expensive), including real-time recurrent learning, echo state networks, and unbiased online recurrent optimization.
Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic
Atcold/pytorch-PPUU
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ICLR 2019
Learning a policy using only observational data is challenging because the distribution of states it induces at execution time may differ from the distribution observed during training.
Deep Global Generalized Gaussian Networks
To handle this issue, this paper proposes a novel deep global generalized Gaussian network (3G-Net), whose core is to estimate a global covariance of generalized Gaussian for modeling the last convolutional activations.
