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Found 10 papers, 7 papers with code
A variable metric proximal stochastic gradient method: an application to classification problems
To control the variance of the objective's gradients, we use an automatic sample size selection along with a variable metric to precondition the stochastic gradient directions.
Majorization-Minimization for sparse SVMs
Several decades ago, Support Vector Machines (SVMs) were introduced for performing binary classification tasks, under a supervised framework.
Uncovering the Background-Induced bias in RGB based 6-DoF Object Pose Estimation
We analyze how the presence of the markers affects the pose estimation accuracy, and how this bias may be mitigated through data augmentation and other methods.
Explainable bilevel optimization: an application to the Helsinki deblur challenge
In this paper we present a bilevel optimization scheme for the solution of a general image deblurring problem, in which a parametric variational-like approach is encapsulated within a machine learning scheme to provide a high quality reconstructed image with automatically learned parameters.
CERBERUS: Simple and Effective All-In-One Automotive Perception Model with Multi Task Learning
Perceiving the surrounding environment is essential for enabling autonomous or assisted driving functionalities.
Constrained and unconstrained deep image prior optimization models with automatic regularization
Numerical results demonstrate the robustness with respect to image content, noise levels and hyperparameters of the proposed models on both denoising and deblurring of simulated as well as real natural and medical images.
DCT-Former: Efficient Self-Attention with Discrete Cosine Transform
Since their introduction the Trasformer architectures emerged as the dominating architectures for both natural language processing and, more recently, computer vision applications.
All You Can Embed: Natural Language based Vehicle Retrieval with Spatio-Temporal Transformers
Combining Natural Language with Vision represents a unique and interesting challenge in the domain of Artificial Intelligence.
Combining Weighted Total Variation and Deep Image Prior for natural and medical image restoration via ADMM
In the last decades, unsupervised deep learning based methods have caught researchers attention, since in many real applications, such as medical imaging, collecting a great amount of training examples is not always feasible.
Mise en abyme with artificial intelligence: how to predict the accuracy of NN, applied to hyper-parameter tuning
This approach can be of particular interest when the space of the characteristics of the network is notably large or when its full training is highly time-consuming.
