Search Results for author:
Found 10 papers, 4 papers with code
Outline-Guided Object Inpainting with Diffusion Models
We achieve that by creating variations of the available annotated object instances in a way that preserves the provided mask annotations, thereby resulting in new image-mask pairs to be added to the set of annotated images.
Combining Data Generation and Active Learning for Low-Resource Question Answering
In this work, we propose a novel approach that combines data augmentation via question-answer generation with Active Learning to improve performance in low-resource settings, where the target domains are diverse in terms of difficulty and similarity to the source domain.
Active Learning for Imbalanced Civil Infrastructure Data
Unfortunately, annotation costs are incredibly high as our proprietary civil engineering dataset must be annotated by highly trained engineers.
Model-Assisted Labeling via Explainability for Visual Inspection of Civil Infrastructures
These are used within an assisted labeling framework where the annotators can interact with them as proposal segmentation masks by deciding to accept, reject or modify them, and interactions are logged as weak labels to further refine the classifier.
Enabling Reproducibility and Meta-learning Through a Lifelong Database of Experiments (LDE)
We present the Lifelong Database of Experiments (LDE) that automatically extracts and stores linked metadata from experiment artifacts and provides features to reproduce these artifacts and perform meta-learning across them.
Generating Efficient DNN-Ensembles with Evolutionary Computation
In this work, we leverage ensemble learning as a tool for the creation of faster, smaller, and more accurate deep learning models.
Reducing Data Motion to Accelerate the Training of Deep Neural Networks
This paper reduces the cost of DNNs training by decreasing the amount of data movement across heterogeneous architectures composed of several GPUs and multicore CPU devices.
Distributed, Parallel, and Cluster Computing
Constrained deep neural network architecture search for IoT devices accounting hardware calibration
We further improve the accuracy to 82. 07% by including 16-bit half types and we obtain the best accuracy of 83. 45% by extending the search with model optimized IEEE 754 reduced types.
BAGAN: Data Augmentation with Balancing GAN
The generator in the GAN is initialized with the encoder module of an autoencoder that enables us to learn an accurate class-conditioning in the latent space.
Efficient Image Dataset Classification Difficulty Estimation for Predicting Deep-Learning Accuracy
In the deep-learning community new algorithms are published at an incredible pace.
