Search Results for author:
Found 17 papers, 2 papers with code
GPT Models in Construction Industry: Opportunities, Limitations, and a Use Case Validation
The findings of the study would be of benefit to researchers, practitioners and stakeholders, as it presents research vistas for LLMs in the construction industry.
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.
Quality Guided Sketch-to-Photo Image Synthesis
We incorporate a hybrid discriminator which performs attribute classification of multiple target attributes, a quality guided encoder that minimizes the perceptual dissimilarity of the latent space embedding of the synthesized and real image at different layers in the network and an identity preserving network that maintains the identity of the synthesised image throughout the training process.
Robust Facial Landmark Detection via Aggregation on Geometrically Manipulated Faces
In this work, we present a practical approach to the problem of facial landmark detection.
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.
A data-driven proxy to Stoke's flow in porous media
To develop the model, the detailed pore space geometry and simulation runs data from 3500 two-dimensional high-fidelity Lattice Boltzmann simulation runs are used to train and to predict the solutions with a high accuracy in much less computational time.
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.
Style and Content Disentanglement in Generative Adversarial Networks
This paper describes the Style and Content Disentangled GAN (SC-GAN), a new unsupervised algorithm for training GANs that learns disentangled style and content representations of the data.
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 Learning-Based Framework for Two-Dimensional Vehicle Maneuver Prediction over V2V Networks
For non-ideal case, an estimation step is included in the framework before the parameter prediction block to handle the drawbacks of packet drops or sensor failures and reconstruct the time series of vehicle parameters at a desirable frequency.
ID Preserving Generative Adversarial Network for Partial Latent Fingerprint Reconstruction
We achieved the rank-10 accuracy of 88. 02\% on the IIIT-Delhi latent fingerprint database for the task of latent-to-latent matching and rank-50 accuracy of 70. 89\% on the IIIT-Delhi MOLF database for the task of latent-to-sensor matching.
Deep Sketch-Photo Face Recognition Assisted by Facial Attributes
The proposed Attribute-Assisted Deep Con- volutional Neural Network (AADCNN) method exploits the facial attributes and leverages the loss functions from the facial attributes identification and face verification tasks in order to learn rich discriminative features in a common em- bedding subspace.
Prosodic-Enhanced Siamese Convolutional Neural Networks for Cross-Device Text-Independent Speaker Verification
In this paper a novel cross-device text-independent speaker verification architecture is proposed.
Multi-Level Feature Abstraction from Convolutional Neural Networks for Multimodal Biometric Identification
Multiple features are extracted at several different convolutional layers from each modality-specific CNN for joint feature fusion, optimization, and classification.
Attribute-Centered Loss for Soft-Biometrics Guided Face Sketch-Photo Recognition
Specifically, an attribute-centered loss is proposed which learns several distinct centers, in a shared embedding space, for photos and sketches with different combinations of attributes.
Deep Cross Polarimetric Thermal-to-visible Face Recognition
we propose a coupled deep neural network architecture which leverages relatively large visible and thermal datasets to overcome the problem of overfitting and eventually we train it by a polarimetric thermal face dataset which is the first of its kind.
Fingerprint Distortion Rectification using Deep Convolutional Neural Networks
Elastic distortion of fingerprints has a negative effect on the performance of fingerprint recognition systems.
