Search Results for author:
Found 11 papers, 9 papers with code
Copycat CNN: Are Random Non-Labeled Data Enough to Steal Knowledge from Black-box Models?
In a preliminary work, we presented a simple, yet powerful, method to copy black-box models by querying them with natural random images.
Deep traffic light detection by overlaying synthetic context on arbitrary natural images
By providing real image samples with traffic context to the network, the model learns to detect and classify elements of interest, such as pedestrians, traffic signs, and traffic lights.
Self-supervised Deep Reconstruction of Mixed Strip-shredded Text Documents
The solution presented in this work extends our previous deep learning method for single-page reconstruction to a more realistic/complex scenario: the reconstruction of several mixed shredded documents at once.
Fast(er) Reconstruction of Shredded Text Documents via Self-Supervised Deep Asymmetric Metric Learning
The reconstruction of shredded documents consists in arranging the pieces of paper (shreds) in order to reassemble the original aspect of such documents.
Effortless Deep Training for Traffic Sign Detection Using Templates and Arbitrary Natural Images
Deep learning has been successfully applied to several problems related to autonomous driving.
Cross-Domain Car Detection Using Unsupervised Image-to-Image Translation: From Day to Night
In this work, a method for training a car detection system with annotated data from a source domain (day images) without requiring the image annotations of the target domain (night images) is presented.
Traffic Light Recognition Using Deep Learning and Prior Maps for Autonomous Cars
However, none of them combine the power of the deep learning-based detectors with prior maps to recognize the state of the relevant traffic lights.
Ego-Lane Analysis System (ELAS): Dataset and Algorithms
The dataset was manually annotated and made publicly available to enable evaluation of several events that are of interest for the research community (i. e., lane estimation, change, and centering; road markings; intersections; LMTs; crosswalks and adjacent lanes).
Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Data
The copy is two-fold: i) the target network is queried with random data and its predictions are used to create a fake dataset with the knowledge of the network; and ii) a copycat network is trained with the fake dataset and should be able to achieve similar performance as the target network.
Automatic Large-Scale Data Acquisition via Crowdsourcing for Crosswalk Classification: A Deep Learning Approach
Many crosswalk classification, detection and localization systems have been proposed in the literature over the years.
Deep Learning Based Large-Scale Automatic Satellite Crosswalk Classification
In this letter, crowdsourcing systems are exploited in order to enable the automatic acquisition and annotation of a large-scale satellite imagery database for crosswalks related tasks.
