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Precision-Recall Divergence Optimization for Generative Modeling with GANs and Normalizing Flows
Achieving a balance between image quality (precision) and diversity (recall) is a significant challenge in the domain of generative models.
Learning Deep Bilinear Transformation for Fine-grained Image Representation
Heliang Zheng, Jianlong Fu, Zheng-Jun Zha, Jiebo Luo
Bilinear feature transformation has shown the state-of-the-art performance in learning fine-grained image representations.
A Bayes-Sard Cubature Method
Toni Karvonen, Chris J. Oates, Simo Sarkka
Neural Information Processing Systems http://nips.cc/
TADAM: Task dependent adaptive metric for improved few-shot learning
Boris Oreshkin, Pau Rodrรญguez Lรณpez, Alexandre Lacoste
Neural Information Processing Systems http://nips.cc/
ScreeningSinkhornAlgorithmforRegularized OptimalTransport
We introduce in this paper a novel strategy for efficiently approximating the Sinkhorn distance between twodiscrete measures.
How SGD Selects the Global Minima in Over-parameterized Learning: A Dynamical Stability Perspective
Neural Information Processing Systems http://nips.cc/
