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Found 9 papers, 5 papers with code
Symbolic Music Generation with Non-Differentiable Rule Guided Diffusion
We propose Stochastic Control Guidance (SCG), a novel guidance method that only requires forward evaluation of rule functions that can work with pre-trained diffusion models in a plug-and-play way, thus achieving training-free guidance for non-differentiable rules for the first time.
FI-ODE: Certifiably Robust Forward Invariance in Neural ODEs
Forward invariance is a long-studied property in control theory that is used to certify that a dynamical system stays within some pre-specified set of states for all time, and also admits robustness guarantees (e. g., the certificate holds under perturbations).
Diffusion Models for Adversarial Purification
Adversarial purification refers to a class of defense methods that remove adversarial perturbations using a generative model.
Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds
Certified robustness is a desirable property for deep neural networks in safety-critical applications, and popular training algorithms can certify robustness of a neural network by computing a global bound on its Lipschitz constant.
Neural Networks with Recurrent Generative Feedback
This mechanism can be interpreted as a form of self-consistency between the maximum a posteriori (MAP) estimation of an internal generative model and the external environment.
Brain-inspired Robust Vision using Convolutional Neural Networks with Feedback
Primates have a remarkable ability to correctly classify images even in the presence of significant noise and degradation.
Out-of-Distribution Detection Using Neural Rendering Generative Models
Our results show that when trained on CIFAR-10, lower likelihood (of latent variables) is assigned to SVHN images.
Semantic-driven Generation of Hyperlapse from $360^\circ$ Video
We present a system for converting a fully panoramic ($360^\circ$) video into a normal field-of-view (NFOV) hyperlapse for an optimal viewing experience.
Pose Invariant Embedding for Deep Person Re-identification
Second, to reduce the impact of pose estimation errors and information loss during PoseBox construction, we design a PoseBox fusion (PBF) CNN architecture that takes the original image, the PoseBox, and the pose estimation confidence as input.
