39 citations · 102 across the 19 of their papers we have counts for
5 papers · 1 filter
Responsible Disclosure of Generative Models Using Scalable Fingerprinting
Ning Yu, Vladislav Skripniuk, Dingfan Chen +2
Over the past years, deep generative models have achieved a new level of performance. Generated data has become difficult, if not impossible, to be distinguished from real data. Wh…
SLADE: A Self-Training Framework For Distance Metric Learning
Jiali Duan, Yen-Liang Lin, Son Tran +2
Most existing distance metric learning approaches use fully labeled data to learn the sample similarities in an embedding space. We present a self-training framework, SLADE, to imp…
Analyzing and Mitigating JPEG Compression Defects in Deep Learning
Max Ehrlich, Larry Davis, Ser-Nam Lim +1
With the proliferation of deep learning methods, many computer vision problems which were considered academic are now viable in the consumer setting. One drawback of consumer appli…
LayoutTransformer: Layout Generation and Completion with Self-attention
Kamal Gupta, Justin Lazarow, Alessandro Achille +3
We address the problem of scene layout generation for diverse domains such as images, mobile applications, documents, and 3D objects. Most complex scenes, natural or human-designed…
Inclusive GAN: Improving Data and Minority Coverage in Generative Models
Ning Yu, Ke Li, Peng Zhou +3
Generative Adversarial Networks (GANs) have brought about rapid progress towards generating photorealistic images. Yet the equitable allocation of their modeling capacity among sub…