11 citations · 12 across the 6 of their papers we have counts for
6 papers
Partial Identifiability for Domain Adaptation
Lingjing Kong, Shaoan Xie, Weiran Yao +5
Unsupervised domain adaptation is critical to many real-world applications where label information is unavailable in the target domain. In general, without further assumptions, the…
Towards Creativity Characterization of Generative Models via Group-based Subset Scanning
Celia Cintas, Payel Das, Brian Quanz +3
Deep generative models, such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs), have been employed widely in computational creativity research. However,…
Pattern Detection in the Activation Space for Identifying Synthesized Content
Celia Cintas, Skyler Speakman, Girmaw Abebe Tadesse +3
Generative Adversarial Networks (GANs) have recently achieved unprecedented success in photo-realistic image synthesis from low-dimensional random noise. The ability to synthesize…
Towards creativity characterization of generative models via group-based subset scanning
Celia Cintas, Payel Das, Brian Quanz +3
Deep generative models, such as Variational Autoencoders (VAEs), have been employed widely in computational creativity research. However, such models discourage out-of-distribution…
Identifying Audio Adversarial Examples via Anomalous Pattern Detection
Victor Akinwande, Celia Cintas, Skyler Speakman +1
Audio processing models based on deep neural networks are susceptible to adversarial attacks even when the adversarial audio waveform is 99.9% similar to a benign sample. Given the…
Characterizing the hyper-parameter space of LSTM language models for mixed context applications
Victor Akinwande, Sekou L. Remy
Applying state of the art deep learning models to novel real world datasets gives a practical evaluation of the generalizability of these models. Of importance in this process is h…