Search Results for author: Eero P. Simoncelli

Found 32 papers, 10 papers with code

Layerwise complexity-matched learning yields an improved model of cortical area V2

no code implementations18 Dec 2023 Nikhil Parthasarathy, Olivier J. Hénaff, Eero P. Simoncelli

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.

Object Recognition

Generalization in diffusion models arises from geometry-adaptive harmonic representations

1 code implementation4 Oct 2023 Zahra Kadkhodaie, Florentin Guth, Eero P. Simoncelli, Stéphane Mallat

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.

Image Denoising Memorization

Adaptive whitening with fast gain modulation and slow synaptic plasticity

1 code implementation NeurIPS 2023 Lyndon R. Duong, Eero P. Simoncelli, Dmitri B. Chklovskii, David Lipshutz

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

no code implementations31 May 2023 Lyndon R. Duong, Colin Bredenberg, David J. Heeger, Eero P. Simoncelli

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

1 code implementation27 Jan 2023 Lyndon R. Duong, David Lipshutz, David J. Heeger, Dmitri B. Chklovskii, Eero P. Simoncelli

Statistical whitening transformations play a fundamental role in many computational systems, and may also play an important role in biological sensory systems.

Developing and Evaluating Deep Neural Network-based Denoising for Nanoparticle TEM Images with Ultra-low Signal-to-Noise

no code implementations19 Jan 2021 Joshua L. Vincent, Ramon Manzorro, Sreyas Mohan, Binh Tang, Dev Y. Sheth, Eero P. Simoncelli, David S. Matteson, Carlos Fernandez-Granda, Peter A. Crozier

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

Self-Supervised Learning of a Biologically-Inspired Visual Texture Model

no code implementations30 Jun 2020 Nikhil Parthasarathy, Eero P. Simoncelli

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.

Self-Supervised Learning Texture Classification

Comparison of Image Quality Models for Optimization of Image Processing Systems

1 code implementation4 May 2020 Keyan Ding, Kede Ma, Shiqi Wang, Eero P. Simoncelli

The performance of objective image quality assessment (IQA) models has been evaluated primarily by comparing model predictions to human quality judgments.

Deblurring Denoising +2

Image Quality Assessment: Unifying Structure and Texture Similarity

2 code implementations16 Apr 2020 Keyan Ding, Kede Ma, Shiqi Wang, Eero P. Simoncelli

Objective measures of image quality generally operate by comparing pixels of a "degraded" image to those of the original.

Image Quality Assessment Retrieval +1

Interpretable and robust blind image denoising with bias-free convolutional neural networks

no code implementations NeurIPS Workshop Deep_Invers 2019 Zahra Kadkhodaie, Sreyas Mohan, Eero P. Simoncelli, Carlos Fernandez-Granda

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.

Image Denoising

Robust and interpretable blind image denoising via bias-free convolutional neural networks

1 code implementation ICLR 2020 Sreyas Mohan, Zahra Kadkhodaie, Eero P. Simoncelli, Carlos Fernandez-Granda

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.

Image Denoising

Eigen-Distortions of Hierarchical Representations

no code implementations NeurIPS 2017 Alexander Berardino, Johannes Ballé, Valero Laparra, Eero P. Simoncelli

We develop a method for comparing hierarchical image representations in terms of their ability to explain perceptual sensitivity in humans.

Object Recognition

Perceptually Optimized Image Rendering

no code implementations23 Jan 2017 Valero Laparra, Alex Berardino, Johannes Ballé, Eero P. Simoncelli

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

13 code implementations5 Nov 2016 Johannes Ballé, Valero Laparra, Eero P. Simoncelli

We describe an image compression method, consisting of a nonlinear analysis transformation, a uniform quantizer, and a nonlinear synthesis transformation.

Image Compression MS-SSIM +1

End-to-end optimization of nonlinear transform codes for perceptual quality

no code implementations18 Jul 2016 Johannes Ballé, Valero Laparra, Eero P. Simoncelli

We introduce a general framework for end-to-end optimization of the rate--distortion performance of nonlinear transform codes assuming scalar quantization.

Quantization

Direct Estimation of Firing Rates from Calcium Imaging Data

no code implementations4 Jan 2016 Elad Ganmor, Michael Krumin, Luigi F. Rossi, Matteo Carandini, Eero P. Simoncelli

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.

Density Modeling of Images using a Generalized Normalization Transformation

2 code implementations19 Nov 2015 Johannes Ballé, Valero Laparra, Eero P. Simoncelli

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.

Geodesics of learned representations

no code implementations19 Nov 2015 Olivier J. Hénaff, Eero P. Simoncelli

We develop a new method for visualizing and refining the invariances of learned representations.

Image Classification Translation

A model of sensory neural responses in the presence of unknown modulatory inputs

no code implementations6 Jul 2015 Neil C. Rabinowitz, Robbe L. T. Goris, Johannes Ballé, Eero P. Simoncelli

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

no code implementations20 Dec 2014 Olivier J. Hénaff, Johannes Ballé, Neil C. Rabinowitz, Eero P. Simoncelli

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.

Denoising

Efficient and direct estimation of a neural subunit model for sensory coding

no code implementations NeurIPS 2012 Brett Vintch, Andrew Zaharia, J Movshon, Eero P. Simoncelli

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

no code implementations NeurIPS 2012 Yan Karklin, Chaitanya Ekanadham, Eero P. Simoncelli

We develop a probabilistic generative model for representing acoustic event structure at multiple scales via a two-stage hierarchy.

Denoising

A blind sparse deconvolution method for neural spike identification

no code implementations NeurIPS 2011 Chaitanya Ekanadham, Daniel Tranchina, Eero P. Simoncelli

Most current methods are based on clustering, which requires substantial human supervision and produces systematic errors by failing to properly handle temporally overlapping spikes.

Clustering

Implicit encoding of prior probabilities in optimal neural populations

no code implementations NeurIPS 2010 Deep Ganguli, Eero P. Simoncelli

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

no code implementations NeurIPS 2009 Matthias Bethge, Eero P. Simoncelli, Fabian H. Sinz

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

no code implementations NeurIPS 2008 Siwei Lyu, Eero P. Simoncelli

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.

Characterizing Neural Gain Control using Spike-triggered Covariance

no code implementations NeurIPS 2001 Odelia Schwartz, E.J. Chichilnisky, Eero P. Simoncelli

Spike-triggered averaging techniques are effective for linear characterization of neural responses.

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