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Found 39 papers, 14 papers with code
Detection of Moving Objects Using Self-motion Constraints on Optic Flow
Consistent with the hypothesis, we found that performance depended on the deviation of the object velocity from the constraint segment, rather than a difference between retinal velocities of the object and its local surround.
Learning predictable and robust neural representations by straightening image sequences
Moreover, these beneficial properties can be transferred to other training procedures by using the straightening objective as a regularizer, suggesting a broader utility for straightening as a principle for robust unsupervised learning.
Video prediction using score-based conditional density estimation
Furthermore, analysis of networks trained on natural image sequences reveals that the representation automatically weights predictive evidence by its reliability, which is a hallmark of statistical inference
Discriminating image representations with principal distortions
This metric may then be used to optimally differentiate a set of models, by finding a pair of "principal distortions" that maximize the variance of the models under this metric.
Feature-guided score diffusion for sampling conditional densities
We demonstrate that the algorithm can generate high quality and diverse samples from the conditioning class.
Shaping the distribution of neural responses with interneurons in a recurrent circuit model
The circuit, which is comprised of primary neurons that are recurrently connected to a set of local interneurons, continuously optimizes this objective by dynamically adjusting both the synaptic connections between neurons as well as the interneuron activation functions.
Generalized Compressed Sensing for Image Reconstruction with Diffusion Probabilistic Models
To exploit such structure, we introduce a general method for obtaining an optimized set of linear measurements for efficient image reconstruction, where the signal statistics are expressed by the prior implicit in a neural network trained to perform denoising (known as a ``diffusion model'').
Layerwise complexity-matched learning yields an improved model of cortical area V2
Finally, when the two-stage model is used as a fixed front-end for a deep network trained to perform object recognition, the resultant model (LCL-V2Net) is significantly better than standard end-to-end self-supervised, supervised, and adversarially-trained models in terms of generalization to out-of-distribution tasks and alignment with human behavior.
Generalization in diffusion models arises from geometry-adaptive harmonic representations
Finally, we show that when trained on regular image classes for which the optimal basis is known to be geometry-adaptive and harmonic, the denoising performance of the networks is near-optimal.
Adaptive whitening with fast gain modulation and slow synaptic plasticity
Neurons in early sensory areas rapidly adapt to changing sensory statistics, both by normalizing the variance of their individual responses and by reducing correlations between their responses.
Adaptive coding efficiency in recurrent cortical circuits via gain control
Using published V1 population adaptation data, we show that propagation of single neuron gain changes in a recurrent network is sufficient to capture the entire set of observed adaptation effects.
Adaptive whitening in neural populations with gain-modulating interneurons
Statistical whitening transformations play a fundamental role in many computational systems, and may also play an important role in biological sensory systems.
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 sigma40
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.
Solving Linear Inverse Problems Using the Prior Implicit in a Denoiser
Here, we develop a robust and general methodology for making use of this implicit prior.
Self-Supervised Learning of a Biologically-Inspired Visual Texture Model
These responses are processed by a second stage (analogous to cortical area V2) consisting of convolutional filters followed by half-wave rectification and pooling to generate V2 'complex cell' responses.
Comparison of Image Quality Models for Optimization of Image Processing Systems
The performance of objective image quality assessment (IQA) models has been evaluated primarily by comparing model predictions to human quality judgments.
Image Quality Assessment: Unifying Structure and Texture Similarity
Objective measures of image quality generally operate by comparing pixels of a "degraded" image to those of the original.
Ranked #31 on
Video Quality Assessment
on MSU SR-QA Dataset
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.
Eigen-Distortions of Hierarchical Representations
We develop a method for comparing hierarchical image representations in terms of their ability to explain perceptual sensitivity in humans.
Perceptually Optimized Image Rendering
We develop a framework for rendering photographic images, taking into account display limitations, so as to optimize perceptual similarity between the rendered image and the original scene.
End-to-end Optimized Image Compression
We describe an image compression method, consisting of a nonlinear analysis transformation, a uniform quantizer, and a nonlinear synthesis transformation.
End-to-end optimization of nonlinear transform codes for perceptual quality
We introduce a general framework for end-to-end optimization of the rate--distortion performance of nonlinear transform codes assuming scalar quantization.
Direct Estimation of Firing Rates from Calcium Imaging Data
This approach can be used to estimate average firing rates or tuning curves directly from the imaging data, and is sufficiently flexible to incorporate prior knowledge about tuning structure.
Geodesics of learned representations
We develop a new method for visualizing and refining the invariances of learned representations.
Density Modeling of Images using a Generalized Normalization Transformation
The data are linearly transformed, and each component is then normalized by a pooled activity measure, computed by exponentiating a weighted sum of rectified and exponentiated components and a constant.
A model of sensory neural responses in the presence of unknown modulatory inputs
Neural responses are highly variable, and some portion of this variability arises from fluctuations in modulatory factors that alter their gain, such as adaptation, attention, arousal, expected or actual reward, emotion, and local metabolic resource availability.
The local low-dimensionality of natural images
We develop a new statistical model for photographic images, in which the local responses of a bank of linear filters are described as jointly Gaussian, with zero mean and a covariance that varies slowly over spatial position.
Efficient and direct estimation of a neural subunit model for sensory coding
Many visual and auditory neurons have response properties that are well explained by pooling the rectified responses of a set of self-similar linear filters.
Hierarchical spike coding of sound
We develop a probabilistic generative model for representing acoustic event structure at multiple scales via a two-stage hierarchy.
A blind sparse deconvolution method for neural spike identification
Most current methods are based on clustering, which requires substantial human supervision and produces systematic errors by failing to properly handle temporally overlapping spikes.
Implicit encoding of prior probabilities in optimal neural populations
Here we consider the influence of a prior probability distribution over sensory variables on the optimal allocation of cells and spikes in a neural population.
Hierarchical Modeling of Local Image Features through L_p-Nested Symmetric Distributions
We introduce a new family of distributions, called $L_p${\em -nested symmetric distributions}, whose densities access the data exclusively through a hierarchical cascade of $L_p$-norms.
Reducing statistical dependencies in natural signals using radial Gaussianization
In this case, no linear transform suffices to properly decompose the signal into independent components, but we show that a simple nonlinear transformation, which we call radial Gaussianization (RG), is able to remove all dependencies.
A Bayesian Model of Conditioned Perception
We propose an extended probabilistic model for human perception.
Characterizing Neural Gain Control using Spike-triggered Covariance
Spike-triggered averaging techniques are effective for linear characterization of neural responses.
