Search Results for author:
Found 11 papers, 7 papers with code
The Tug-of-War Between Deepfake Generation and Detection
We advocate for a proactive approach in the "tug-of-war" between deepfake creators and detectors, emphasizing the need for continuous research collaboration, standardization of evaluation metrics, and the creation of comprehensive benchmarks.
DistilDIRE: A Small, Fast, Cheap and Lightweight Diffusion Synthesized Deepfake Detection
A dramatic influx of diffusion-generated images has marked recent years, posing unique challenges to current detection technologies.
Let's Go Shopping (LGS) -- Web-Scale Image-Text Dataset for Visual Concept Understanding
Vision and vision-language applications of neural networks, such as image classification and captioning, rely on large-scale annotated datasets that require non-trivial data-collecting processes.
Improving the Accuracy-Robustness Trade-Off of Classifiers via Adaptive Smoothing
While prior research has proposed a plethora of methods that build neural classifiers robust against adversarial robustness, practitioners are still reluctant to adopt them due to their unacceptably severe clean accuracy penalties.
Ranked #1 on
Adversarial Robustness
on CIFAR-100
(using extra training data)
GlideNet: Global, Local and Intrinsic based Dense Embedding NETwork for Multi-category Attributes Prediction
Collectively, the Global-Local-Intrinsic blocks comprehend the scene's global context while attending to the characteristics of the local object of interest.
CAR -- Cityscapes Attributes Recognition A Multi-category Attributes Dataset for Autonomous Vehicles
We have also created an API for the dataset to ease the usage of CAR.
Natural Adversarial Objects
Moreover, by comparing a variety of object detection architectures, we find that better performance on MSCOCO validation set does not necessarily translate to better performance on NAO, suggesting that robustness cannot be simply achieved by training a more accurate model.
Ranked #1 on
Object Detection
on NAO
On The State of Data In Computer Vision: Human Annotations Remain Indispensable for Developing Deep Learning Models
High-quality labeled datasets play a crucial role in fueling the development of machine learning (ML), and in particular the development of deep learning (DL).
Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset
We train a deep neural network model to classify 114 skin conditions and find that the model is most accurate on skin types similar to those it was trained on.
Breaking hypothesis testing for failure rates
Next, we compare the performance of the rate test to a version of the Wald test customized to the Negative Binomial point process and find it to perform very similarly while being much more general and versatile.
Stochastic Answer Networks for SQuAD 2.0
This paper presents an extension of the Stochastic Answer Network (SAN), one of the state-of-the-art machine reading comprehension models, to be able to judge whether a question is unanswerable or not.
