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Found 19 papers, 8 papers with code
Reconstructing the Noise Variance Manifold for Image Denoising
Deep Convolutional Neural Networks (CNNs) have been successfully used in many low-level vision problems like image denoising.
MI-NeRF: Learning a Single Face NeRF from Multiple Identities
In this work, we introduce a method that learns a single dynamic neural radiance field (NeRF) from monocular talking face videos of multiple identities.
Generalization of Scaled Deep ResNets in the Mean-Field Regime
To derive the generalization bounds under this setting, our analysis necessitates a shift from the conventional time-invariant Gram matrix employed in the lazy training regime to a time-variant, distribution-dependent version.
Multilinear Mixture of Experts: Scalable Expert Specialization through Factorization
The Mixture of Experts (MoE) paradigm provides a powerful way to decompose inscrutable dense layers into smaller, modular computations often more amenable to human interpretation, debugging, and editability.
Leveraging the Context through Multi-Round Interactions for Jailbreaking Attacks
We contend that the prior context--the information preceding the attack query--plays a pivotal role in enabling potent Jailbreaking attacks.
Multilinear Operator Networks
On the other hand, Polynomial Networks is a class of models that does not require activation functions, but have yet to perform on par with modern architectures.
Efficient local linearity regularization to overcome catastrophic overfitting
Our regularization term can be theoretically linked to curvature of the loss function and is computationally cheaper than previous methods by avoiding Double Backpropagation.
Revisiting adversarial training for the worst-performing class
Despite progress in adversarial training (AT), there is a substantial gap between the top-performing and worst-performing classes in many datasets.
Self-Supervised Neural Architecture Search for Imbalanced Datasets
The results demonstrate how the proposed method can be used in imbalanced datasets, while it can be fully run on a single GPU.
Tensor Methods in Computer Vision and Deep Learning
Tensors, or multidimensional arrays, are data structures that can naturally represent visual data of multiple dimensions.
Unsupervised Controllable Generation with Self-Training
Instead, we propose an unsupervised framework to learn a distribution of latent codes that control the generator through self-training.
P-nets: Deep Polynomial Neural Networks
Deep Convolutional Neural Networks (DCNNs) is currently the method of choice both for generative, as well as for discriminative learning in computer vision and machine learning.
$Π-$nets: Deep Polynomial Neural Networks
Deep Convolutional Neural Networks (DCNNs) is currently the method of choice both for generative, as well as for discriminative learning in computer vision and machine learning.
Ranked #1 on
Graph Representation Learning
on COMA
Reconstructing the Noise Manifold for Image Denoising
Deep Convolutional Neural Networks (CNNs) have been successfully used in many low-level vision problems like image denoising.
Robust Conditional Generative Adversarial Networks
Conditional generative adversarial networks (cGAN) have led to large improvements in the task of conditional image generation, which lies at the heart of computer vision.
Motion deblurring of faces
Notwithstanding, a much less standing mode of variation is motion deblurring, which however presents substantial challenges in face analysis.
Visual Data Augmentation through Learning
In addition, the state-of-the-art data-driven methods demand a vast amount of data, hence a standard engineering trick employed is artificial data augmentation for instance by adding into the data cropped and (affinely) transformed images.
Deep Face Deblurring
Blind deblurring consists a long studied task, however the outcomes of generic methods are not effective in real world blurred images.
A Comprehensive Performance Evaluation of Deformable Face Tracking "In-the-Wild"
Recently, technologies such as face detection, facial landmark localisation and face recognition and verification have matured enough to provide effective and efficient solutions for imagery captured under arbitrary conditions (referred to as "in-the-wild").
