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Found 27 papers, 3 papers with code
Transporting Labels via Hierarchical Optimal Transport for Semi-Supervised Learning
Semi-Supervised Learning (SSL) based on Convolutional Neural Networks (CNNs) have recently been proven as powerful tools for standard tasks such as image classification when there is not a sufficient amount of labeled data available during the training.
Revisiting Outer Optimization in Adversarial Training
This phenomenon hinders the outer optimization in AT since the convergence rate of MSGD is highly dependent on the variance of the gradients.
Controllable 3D Generative Adversarial Face Model via Disentangling Shape and Appearance
3D face modeling has been an active area of research in computer vision and computer graphics, fueling applications ranging from facial expression transfer in virtual avatars to synthetic data generation.
Quality-Aware Multimodal Biometric Recognition
The first loss assures that the representations of modalities for a class have comparable magnitudes to provide a better quality estimation, while the multimodal representations of different classes are distributed to achieve maximum discrimination in the embedding space.
Attribute Guided Sparse Tensor-Based Model for Person Re-Identification
In this paper, we propose a new method to leverage features from human attributes for person ReID.
Tasks Structure Regularization in Multi-Task Learning for Improving Facial Attribute Prediction
To address this problem, we use a new Multi-Task Learning (MTL) paradigm in which a facial attribute predictor uses the knowledge of other related attributes to obtain a better generalization performance.
Profile to Frontal Face Recognition in the Wild Using Coupled Conditional GAN
We have evaluated the performance of cpCNN and ADDA and compared it with the proposed cpGAN.
Self-Supervised Wasserstein Pseudo-Labeling for Semi-Supervised Image Classification
The goal is to use Wasserstein metric to provide pseudo labels for the unlabeled images to train a Convolutional Neural Networks (CNN) in a Semi-Supervised Learning (SSL) manner for the classification task.
Matching Distributions via Optimal Transport for Semi-Supervised Learning
Semi-Supervised Learning (SSL) approaches have been an influential framework for the usage of unlabeled data when there is not a sufficient amount of labeled data available over the course of training.
Mutual Information Maximization on Disentangled Representations for Differential Morph Detection
The network is trained by triplets of face images, in which the intermediate image inherits the landmarks from one image and the appearance from the other image.
Cross-Spectral Iris Matching Using Conditional Coupled GAN
Cross-spectral iris recognition is emerging as a promising biometric approach to authenticating the identity of individuals.
PF-cpGAN: Profile to Frontal Coupled GAN for Face Recognition in the Wild
In this paper, we hypothesize that the profile face domain possesses a gradual connection with the frontal face domain in the deep feature space.
Error-Corrected Margin-Based Deep Cross-Modal Hashing for Facial Image Retrieval
The DNDCMH network consists of two separatecomponents: an attribute-based deep cross-modal hashing (ADCMH) module, which uses a margin (m)-based loss function toefficiently learn compact binary codes to preserve similarity between modalities in the Hamming space, and a neural error correctingdecoder (NECD), which is an error correcting decoder implemented with a neural network.
SuperMix: Supervising the Mixing Data Augmentation
On the distillation task, solely classifying images mixed using the teacher's knowledge achieves comparable performance to the state-of-the-art distillation methods.
Boosting Deep Face Recognition via Disentangling Appearance and Geometry
We demonstrate that the proposed approach enhances the performance of deep face recognition models by assisting the training process in two ways.
SmoothFool: An Efficient Framework for Computing Smooth Adversarial Perturbations
Deep neural networks are susceptible to adversarial manipulations in the input domain.
A Weakly Supervised Fine Label Classifier Enhanced by Coarse Supervision
Our deep model uses coarse images in conjunction with fine images to jointly explore the low rank and sparse subspaces by sharing the parameters during the training which causes the data points obtained by the CNN to be well-projected to both sparse and low rank subspaces for classification.
Identity-Aware Deep Face Hallucination via Adversarial Face Verification
First, we propose a multi-scale generator architecture for face hallucination with a high up-scaling ratio factor, which has multiple intermediate outputs at different resolutions.
Deep Sparse Band Selection for Hyperspectral Face Recognition
In this book chapter, we propose a new Convolutional Neural Network (CNN) framework which adopts a structural sparsity learning technique to select the optimal spectral bands to obtain the best face recognition performance over all of the spectral bands.
Attribute-Guided Coupled GAN for Cross-Resolution Face Recognition
In this paper, we propose a novel attribute-guided cross-resolution (low-resolution to high-resolution) face recognition framework that leverages a coupled generative adversarial network (GAN) structure with adversarial training to find the hidden relationship between the low-resolution and high-resolution images in a latent common embedding subspace.
Using Deep Cross Modal Hashing and Error Correcting Codes for Improving the Efficiency of Attribute Guided Facial Image Retrieval
With benefits of fast query speed and low storage cost, hashing-based image retrieval approaches have garnered considerable attention from the research community.
Unsupervised Image-to-Image Translation Using Domain-Specific Variational Information Bound
These approaches usually fail to model domain-specific information which has no representation in the target domain.
Unsupervised Facial Geometry Learning for Sketch to Photo Synthesis
In contrast to current unsupervised image-to-image translation techniques, our framework leverages a novel perceptual discriminator to learn the geometry of human face.
A Deep Face Identification Network Enhanced by Facial Attributes Prediction
In this paper, we propose a new deep framework which predicts facial attributes and leverage it as a soft modality to improve face identification performance.
Blood capillaries and vessels segmentation in optical coherence tomography angiogram using fuzzy C-means and Curvelet transform
This paper has been removed from arXiv as the submitter did not have ownership of the data presented in this work.
A probabilistic patch based image representation using Conditional Random Field model for image classification
Second, the sequence of these ordered patches is modeled as a probabilistic feature vector by CRF to model spatial relationship of these local properties.
An ensemble learning method for scene classification based on Hidden Markov Model image representation
In this work, we propose an efficient image representation model for classification.
