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Found 18 papers, 6 papers with code
SteinDreamer: Variance Reduction for Text-to-3D Score Distillation via Stein Identity
In this paper, we reveal that the gradient estimation in score distillation is inherent to high variance.
Taming Mode Collapse in Score Distillation for Text-to-3D Generation
In this paper, we reveal that the existing score distillation-based text-to-3D generation frameworks degenerate to maximal likelihood seeking on each view independently and thus suffer from the mode collapse problem, manifesting as the Janus artifact in practice.
MMG-Ego4D: Multi-Modal Generalization in Egocentric Action Recognition
In this paper, we study a novel problem in egocentric action recognition, which we term as "Multimodal Generalization" (MMG).
MMG-Ego4D: Multimodal Generalization in Egocentric Action Recognition
In this paper, we study a novel problem in egocentric action recognition, which we term as "Multimodal Generalization" (MMG).
Evaluating Unsupervised Denoising Requires Unsupervised Metrics
In this work, we propose two novel metrics: the unsupervised mean squared error (MSE) and the unsupervised peak signal-to-noise ratio (PSNR), which are computed using only noisy data.
Deep Probability Estimation
Reliable probability estimation is of crucial importance in many real-world applications where there is inherent (aleatoric) uncertainty.
Perturbation CheckLists for Evaluating NLG Evaluation Metrics
Natural Language Generation (NLG) evaluation is a multifaceted task requiring assessment of multiple desirable criteria, e. g., fluency, coherency, coverage, relevance, adequacy, overall quality, etc.
Adaptive Denoising via GainTuning
Deep convolutional neural networks (CNNs) for image denoising are usually trained on large datasets.
Developing and Evaluating Deep Neural Network-based Denoising for Nanoparticle TEM Images with Ultra-low Signal-to-Noise
This shows that the network exploits global and local information in the noisy measurements, for example, by adapting its filtering approach when it encounters atomic-level defects at the nanoparticle surface.
Denoising
Materials Science
Image and Video Processing
Unsupervised Deep Video Denoising
This is advantageous because motion compensation is computationally expensive, and can be unreliable when the input data are noisy.
Ranked #5 on
Video Denoising
on Set8 sigma50
Deep Denoising For Scientific Discovery: A Case Study In Electron Microscopy
SBD outperforms existing techniques by a wide margin on a simulated benchmark dataset, as well as on real data.
Be Like Water: Robustness to Extraneous Variables Via Adaptive Feature Normalization
Extraneous variables are variables that are irrelevant for a certain task, but heavily affect the distribution of the available data.
Knee Cartilage Segmentation Using Diffusion-Weighted MRI
We benchmark our model against a human expert test-retest segmentation and conclude that our model is superior for Patellar and Tibial cartilage using dice score as the comparison metric.
Interpretable and robust blind image denoising with bias-free convolutional neural networks
Here, however, we show that bias terms used in most CNNs (additive constants, including those used for batch normalization) interfere with the interpretability of these networks, do not help performance, and in fact prevent generalization of performance to noise levels not including in the training data.
Robust and interpretable blind image denoising via bias-free convolutional neural networks
In contrast, a bias-free architecture -- obtained by removing the constant terms in every layer of the network, including those used for batch normalization-- generalizes robustly across noise levels, while preserving state-of-the-art performance within the training range.
Data-driven Estimation of Sinusoid Frequencies
Frequency estimation is a fundamental problem in signal processing, with applications in radar imaging, underwater acoustics, seismic imaging, and spectroscopy.
Blind nonnegative source separation using biological neural networks
Blind source separation, i. e. extraction of independent sources from a mixture, is an important problem for both artificial and natural signal processing.
Data Driven Coded Aperture Design for Depth Recovery
To address this drawback we propose a data driven approach for learning the optimal aperture pattern to recover depth map from a single coded image.
