| 1. |
- Tsirikoglou, Apostolia, 1985-, et al.
(författare)
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Differential appearance editing for measured BRDFs
- 2016
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Data driven reflectance models using BRDF data measured from real materials, e.g. [Matusik et al. 2003], are becoming increasingly popular in product visualization, digital design and other applications driven by the need for predictable rendering and highly realistic results. Although recent analytic, parametric BRDFs provide good approximations for many materials, some effects are still not captured well [Löw et al. 2012]. Thus, it is hard to accurately model real materials using analytic models, even if the parameters are fitted to data. In practice, it is often desirable to apply small edits to the measured data for artistic purposes, or to model similar materials that are not available in measured form. A drawback of data driven models is that they are often difficult to edit and do not easily lend themselves well to artistic adjustments. Existing editing techniques for measured data [Schmidt et al. 2014], often use complex decompositions making them difficult to use in practice.
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| 2. |
- Baravdish, Gabriel, 1992-, et al.
(författare)
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GPU Accelerated Sparse Representation of Light Fields
- 2019
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Ingår i: VISIGRAPP - 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Prague, Czech Republic, February 25-27, 2019.. - : SCITEPRESS. - 9789897583544 ; , s. 177-182
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Konferensbidrag (refereegranskat)abstract
- We present a method for GPU accelerated compression of light fields. The approach is by using a dictionary learning framework for compression of light field images. The large amount of data storage by capturing light fields is a challenge to compress and we seek to accelerate the encoding routine by GPGPU computations. We compress the data by projecting each data point onto a set of trained multi-dimensional dictionaries and seek the most sparse representation with the least error. This is done by a parallelization of the tensor-matrix product computed on the GPU. An optimized greedy algorithm to suit computations on the GPU is also presented. The encoding of the data is done segmentally in parallel for a faster computation speed while maintaining the quality. The results shows an order of magnitude faster encoding time compared to the results in the same research field. We conclude that there are further improvements to increase the speed, and thus it is not too far from an interacti ve compression speed.
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| 3. |
- Baravdish, George, 1964-, et al.
(författare)
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Learning via nonlinear conjugate gradients and depth-varying neural ODEs
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- The inverse problem of supervised reconstruction of depth-variable (time-dependent) parameters in a neural ordinary differential equation (NODE) is considered, that means finding the weights of a residual network with time continuous layers. The NODE is treated as an isolated entity describing the full network as opposed to earlier research, which embedded it between pre- and post-appended layers trained by conventional methods. The proposed parameter reconstruction is done for a general first order differential equation by minimizing a cost functional covering a variety of loss functions and penalty terms. A nonlinear conjugate gradient method (NCG) is derived for the minimization. Mathematical properties are stated for the differential equation and the cost functional. The adjoint problem needed is derived together with a sensitivity problem. The sensitivity problem can estimate changes in the network output under perturbation of the trained parameters. To preserve smoothness during the iterations the Sobolev gradient is calculated and incorporated. As a proof-of-concept, numerical results are included for a NODE and two synthetic datasets, and compared with standard gradient approaches (not based on NODEs). The results show that the proposed method works well for deep learning with infinite numbers of layers, and has built-in stability and smoothness.
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| 4. |
- Eilertsen, Gabriel, 1984-, et al.
(författare)
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BriefMatch: Dense binary feature matching for real-time optical flow estimation
- 2017
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Ingår i: Proceedings of the Scandinavian Conference on Image Analysis (SCIA17). - Cham : Springer. - 9783319591254 ; , s. 221-233
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Konferensbidrag (refereegranskat)abstract
- Research in optical flow estimation has to a large extent focused on achieving the best possible quality with no regards to running time. Nevertheless, in a number of important applications the speed is crucial. To address this problem we present BriefMatch, a real-time optical flow method that is suitable for live applications. The method combines binary features with the search strategy from PatchMatch in order to efficiently find a dense correspondence field between images. We show that the BRIEF descriptor provides better candidates (less outlier-prone) in shorter time, when compared to direct pixel comparisons and the Census transform. This allows us to achieve high quality results from a simple filtering of the initially matched candidates. Currently, BriefMatch has the fastest running time on the Middlebury benchmark, while placing highest of all the methods that run in shorter than 0.5 seconds.
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| 5. |
- Eilertsen, Gabriel, 1984-, et al.
(författare)
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Classifying the classifier : dissecting the weight space of neural networks
- 2020
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Ingår i: Proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020). - : IOS PRESS. - 9781643681016 ; , s. 1119-1126
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Konferensbidrag (refereegranskat)abstract
- This paper presents an empirical study on the weights of neural networks, where we interpret each model as a point in a high-dimensional space – the neural weight space. To explore the complex structure of this space, we sample from a diverse selection of training variations (dataset, optimization procedure, architecture,etc.) of neural network classifiers, and train a large number of models to represent the weight space. Then, we use a machine learning approach for analyzing and extracting information from this space. Most centrally, we train a number of novel deep meta-classifiers withthe objective of classifying different properties of the training setup by identifying their footprints in the weight space. Thus, the meta-classifiers probe for patterns induced by hyper-parameters, so that we can quantify how much, where, and when these are encoded through the optimization process. This provides a novel and complementary view for explainable AI, and we show how meta-classifiers can reveal a great deal of information about the training setup and optimization, by only considering a small subset of randomly selected consecutive weights. To promote further research on the weight space, we release the neural weight space (NWS) dataset – a collection of 320K weightsnapshots from 16K individually trained deep neural networks.
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| 6. |
- Eilertsen, Gabriel, 1984-, et al.
(författare)
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Ensembles of GANs for synthetic training data generation
- 2021
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Konferensbidrag (refereegranskat)abstract
- Insufficient training data is a major bottleneck for most deep learning practices, not least in medical imaging where data is difficult to collect and publicly available datasets are scarce due to ethics and privacy. This work investigates the use of synthetic images, created by generative adversarial networks (GANs), as the only source of training data. We demonstrate that for this application, it is of great importance to make use of multiple GANs to improve the diversity of the generated data, i.e. to sufficiently cover the data distribution. While a single GAN can generate seemingly diverse image content, training on this data in most cases lead to severe over-fitting. We test the impact of ensembled GANs on synthetic 2D data as well as common image datasets (SVHN and CIFAR-10), and using both DCGANs and progressively growing GANs. As a specific use case, we focus on synthesizing digital pathology patches to provide anonymized training data.
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| 7. |
- Eilertsen, Gabriel, et al.
(författare)
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Evaluation of tone mapping operators for HDR video
- 2016. - 1st
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Ingår i: High dynamic range video. - London, United Kingdom : Academic Press. - 9780081004128 ; , s. 185-206
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Tone mapping of HDR-video is a challenging filtering problem. It is highly important to develop a framework for evaluation and comparison of tone mapping operators. This chapter gives an overview of different approaches for how evalation of tone mapping operators can be conducted, including experimental setups, choice of input data, choice of tone mapping operators, and the importance of parameter tweaking for fair comparisons. This chapter also gives examples of previous evaluations with a focus on the results from the most recent evaluation conducted by Eilertsen et. al [reference]. This results in a classification of the currently most commonly used tone mapping operators and overview of their performance and possible artifacts.
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| 8. |
- Eilertsen, Gabriel, 1984-, et al.
(författare)
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Model-invariant Weight Distribution Descriptors for Visual Exploration of Neural Networks en Masse
- 2024
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Ingår i: EuroVis 2024 - Short Papers. - 9783038682516
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Konferensbidrag (refereegranskat)abstract
- We present a neural network representation which can be used for visually analyzing the similarities and differences in a large corpus of trained neural networks. The focus is on architecture-invariant comparisons based on network weights, estimating similarities of the statistical footprints encoded by the training setups and stochastic optimization procedures. To make this possible, we propose a novel visual descriptor of neural network weights. The visual descriptor considers local weight statistics in a model-agnostic manner by encoding the distribution of weights over different model depths. We show how such a representation can extract descriptive information, is robust to different parameterizations of a model, and is applicable to different architecture specifications. The descriptor is used to create a model atlas by projecting a model library to a 2D representation, where clusters can be found based on similar weight properties. A cluster analysis strategy makes it possible to understand the weight properties of clusters and how these connect to the different datasets and hyper-parameters used to train the models.
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| 9. |
- Eilertsen, Gabriel, et al.
(författare)
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Perceptually based parameter adjustments for video processing operations
- 2014
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Ingår i: ACM SIGGRAPH Talks 2014. - : ACM Press.
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Konferensbidrag (refereegranskat)abstract
- Extensive post processing plays a central role in modern video production pipelines. A problem in this context is that many filters and processing operators are very sensitive to parameter settings and that the filter responses in most cases are highly non-linear. Since there is no general solution for performing perceptual calibration of image and video operators automatically, it is often necessary to manually perform tweaking of multiple parameters. This is an iterative process which requires instant visual feedback of the result in both the spatial and temporal domains. Due to large filter kernels, computational complexity, high frame rate, and image resolution it is, however, often very time consuming to iteratively re-process and tweak long video sequences.We present a new method for rapidly finding the perceptual minima in high-dimensional parameter spaces of general video operators. The key idea of our algorithm is that the characteristics of an operator can be accurately described by interpolating between a small set of pre-computed parameter settings. By computing a perceptual linearization of the parameter space of a video operator, the user can explore this interpolated space to find the best set of parameters in a robust way. Since many operators are dependent on two or more parameters, we formulate this as a general optimization problem where we let the objective function be determined by the user’s image assessments. To demonstrate the usefulness of our approach we show a set of use cases (see the supplementary material) where our algorithm is applied to computationally expensive video operations.
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| 10. |
- Eilertsen, Gabriel, et al.
(författare)
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Real-time noise-aware tone mapping
- 2015
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Ingår i: ACM Transactions on Graphics. - New York, NY, USA : Association for Computing Machinery (ACM). - 0730-0301 .- 1557-7368. ; 34:6, s. 198:1-198:15
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Tidskriftsartikel (refereegranskat)abstract
- Real-time high quality video tone mapping is needed for manyapplications, such as digital viewfinders in cameras, displayalgorithms which adapt to ambient light, in-camera processing,rendering engines for video games and video post-processing. We propose a viable solution for these applications by designing a videotone-mapping operator that controls the visibility of the noise,adapts to display and viewing environment, minimizes contrastdistortions, preserves or enhances image details, and can be run inreal-time on an incoming sequence without any preprocessing. To ourknowledge, no existing solution offers all these features. Our novelcontributions are: a fast procedure for computing local display-adaptivetone-curves which minimize contrast distortions, a fast method for detailenhancement free from ringing artifacts, and an integrated videotone-mapping solution combining all the above features.
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