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Found 22 papers, 8 papers with code
Solving Masked Jigsaw Puzzles with Diffusion Vision Transformers
Solving image and video jigsaw puzzles poses the challenging task of rearranging image fragments or video frames from unordered sequences to restore meaningful images and video sequences.
Inferring Relational Potentials in Interacting Systems
In this work, we propose Neural Interaction Inference with Potentials (NIIP) as an alternative approach to discover such interactions that enables greater flexibility in trajectory modeling: it discovers a set of relational potentials, represented as energy functions, which when minimized reconstruct the original trajectory.
Learning Transferable Object-Centric Diffeomorphic Transformations for Data Augmentation in Medical Image Segmentation
To this end, we propose a novel object-centric data augmentation model that is able to learn the shape variations for the objects of interest and augment the object in place without modifying the rest of the image.
Geometry of Score Based Generative Models
We demonstrate that looking from geometric perspective enables us to answer many of these questions and provide new interpretations to some known results.
Divide and Compose with Score Based Generative Models
Towards the direction of having more control over image manipulation and conditional generation, we propose to learn image components in an unsupervised manner so that we can compose those components to generate and manipulate images in informed manner.
Boundary-Aware Uncertainty for Feature Attribution Explainers
In this work we propose the Gaussian Process Explanation UnCertainty (GPEC) framework, which generates a unified uncertainty estimate combining decision boundary-aware uncertainty with explanation function approximation uncertainty.
Unsupervised Approaches for Out-Of-Distribution Dermoscopic Lesion Detection
There are limited works showing the efficacy of unsupervised Out-of-Distribution (OOD) methods on complex medical data.
Self-Supervised Decomposition, Disentanglement and Prediction of Video Sequences while Interpreting Dynamics: A Koopman Perspective
Human interpretation of the world encompasses the use of symbols to categorize sensory inputs and compose them in a hierarchical manner.
Reliable Estimation of KL Divergence using a Discriminator in Reproducing Kernel Hilbert Space
To achieve this objective, we 1) present a novel construction of the discriminator in the Reproducing Kernel Hilbert Space (RKHS), 2) theoretically relate the error probability bound of the KL estimates to the complexity of the discriminator in the RKHS space, 3) present a scalable way to control the complexity (RKHS norm) of the discriminator for a reliable estimation of KL divergence, and 4) prove the consistency of the proposed estimator.
Enhancing Mixup-based Semi-Supervised Learning with Explicit Lipschitz Regularization
On three benchmark data sets and one real-world biomedical data set, we demonstrate that this combined regularization results in improved generalization performance of SSL when learning from a small amount of labeled data.
Learning Invariant Feature Representation to Improve Generalization across Chest X-ray Datasets
Through pneumonia-classification experiments on multi-source chest X-ray datasets, we show that this algorithm helps in improving classification accuracy on a new source of X-ray dataset.
Learning Geometry-Dependent and Physics-Based Inverse Image Reconstruction
However, many reconstruction problems involve imaging physics that are dependent on the underlying non-Euclidean geometry.
Semi-supervised Medical Image Classification with Global Latent Mixing
In this work, we argue that regularizing the global smoothness of neural functions by filling the void in between data points can further improve SSL.
High-dimensional Bayesian Optimization of Personalized Cardiac Model Parameters via an Embedded Generative Model
The estimation of patient-specific tissue properties in the form of model parameters is important for personalized physiological models.
Analysis of Discriminator in RKHS Function Space for Kullback-Leibler Divergence Estimation
Based on this theory, we then present a scalable way to control the complexity of the discriminator for a reliable estimation of KL divergence.
Improving Disentangled Representation Learning with the Beta Bernoulli Process
We note that the independence within and the complexity of the latent density are two different properties we constrain when regularizing the posterior density: while the former promotes the disentangling ability of VAE, the latter -- if overly limited -- creates an unnecessary competition with the data reconstruction objective in VAE.
Semi-Supervised Learning by Disentangling and Self-Ensembling Over Stochastic Latent Space
In this work, we hypothesize -- from the generalization perspective -- that self-ensembling can be improved by exploiting the stochasticity of a disentangled latent space.
Bayesian Optimization on Large Graphs via a Graph Convolutional Generative Model: Application in Cardiac Model Personalization
In this paper, we present a novel graph convolutional VAE to allow generative modeling of non-Euclidean data, and utilize it to embed Bayesian optimization of large graphs into a small latent space.
Generative Modeling and Inverse Imaging of Cardiac Transmembrane Potential
We introduce a novel model-constrained inference framework that replaces conventional physiological models with a deep generative model trained to generate TMP sequences from low-dimensional generative factors.
Improving Generalization of Deep Networks for Inverse Reconstruction of Image Sequences
Deep learning networks have shown state-of-the-art performance in many image reconstruction problems.
Deep Generative Model with Beta Bernoulli Process for Modeling and Learning Confounding Factors
While deep representation learning has become increasingly capable of separating task-relevant representations from other confounding factors in the data, two significant challenges remain.
Improving Generalization of Sequence Encoder-Decoder Networks for Inverse Imaging of Cardiac Transmembrane Potential
The results demonstrate that the generalization ability of an inverse reconstruction network can be improved by constrained stochasticity combined with global aggregation of temporal information in the latent space.
