72 citations · 301 across the 26 of their papers we have counts for
7 papers · 1 filter
SIO: Synthetic In-Distribution Data Benefits Out-of-Distribution Detection
Jingyang Zhang, Nathan Inkawhich, Randolph Linderman +3
Building up reliable Out-of-Distribution (OOD) detectors is challenging, often requiring the use of OOD data during training. In this work, we develop a data-driven approach which…
The Untapped Potential of Off-the-Shelf Convolutional Neural Networks
Matthew Inkawhich, Nathan Inkawhich, Eric Davis +2
Over recent years, a myriad of novel convolutional network architectures have been developed to advance state-of-the-art performance on challenging recognition tasks. As computatio…
ScaleNAS: One-Shot Learning of Scale-Aware Representations for Visual Recognition
Hsin-Pai Cheng, Feng Liang, Meng Li +5
Scale variance among different sizes of body parts and objects is a challenging problem for visual recognition tasks. Existing works usually design dedicated backbone or apply Neur…
PENNI: Pruned Kernel Sharing for Efficient CNN Inference
Shiyu Li, Edward Hanson, Hai Li +1
Although state-of-the-art (SOTA) CNNs achieve outstanding performance on various tasks, their high computation demand and massive number of parameters make it difficult to deploy t…
Defending against GAN-based Deepfake Attacks via Transformation-aware Adversarial Faces
Chaofei Yang, Lei Ding, Yiran Chen +1
Deepfake represents a category of face-swapping attacks that leverage machine learning models such as autoencoders or generative adversarial networks. Although the concept of the f…
Conditional Transferring Features: Scaling GANs to Thousands of Classes with 30% Less High-quality Data for Training
Chunpeng Wu, Wei Wen, Yiran Chen +1
Generative adversarial network (GAN) has greatly improved the quality of unsupervised image generation. Previous GAN-based methods often require a large amount of high-quality trai…