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Found 28 papers, 17 papers with code
A multi-stage semi-supervised learning for ankle fracture classification on CT images
Because of the complicated mechanism of ankle injury, it is very difficult to diagnose ankle fracture in clinic.
Dual Memory Networks: A Versatile Adaptation Approach for Vision-Language Models
In this paper, we introduce a versatile adaptation approach that can effectively work under all three settings.
Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation for Semi-Supervised Medical Image Segmentation
In this paper, we present a novel semi-supervised learning method, Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation (DCPA), for medical image segmentation.
A New Benchmark: On the Utility of Synthetic Data with Blender for Bare Supervised Learning and Downstream Domain Adaptation
Moreover, we use the simulation-to-reality adaptation as a downstream task for comparing the transferability between synthetic and real data when used for pre-training, which demonstrates that synthetic data pre-training is also promising to improve real test results.
SR-init: An interpretable layer pruning method
Our SR-init method is inspired by the discovery that the accuracy drop due to stochastic re-initialization of layer parameters differs in various layers.
Unsupervised Domain Adaptation via Distilled Discriminative Clustering
Differently, motivated by the fundamental assumption for domain adaptability, we re-cast the domain adaptation problem as discriminative clustering of target data, given strong privileged information provided by the closely related, labeled source data.
Transferring Dual Stochastic Graph Convolutional Network for Facial Micro-expression Recognition
To improve the recognition performance of the small micro-expression data, this paper presents a transferring dual stochastic Graph Convolutional Network (TDSGCN) model.
Towards Discovering the Effectiveness of Moderately Confident Samples for Semi-Supervised Learning
To answer these problems, we propose a novel Taylor expansion inspired filtration (TEIF) framework, which admits the samples of moderate confidence with similar feature or gradient to the respective one averaged over the labeled and highly confident unlabeled data.
MEGAN: Memory Enhanced Graph Attention Network for Space-Time Video Super-Resolution
Space-time video super-resolution (STVSR) aims to construct a high space-time resolution video sequence from the corresponding low-frame-rate, low-resolution video sequence.
Geometry-Aware Self-Training for Unsupervised Domain Adaptationon Object Point Clouds
The point cloud representation of an object can have a large geometric variation in view of inconsistent data acquisition procedure, which thus leads to domain discrepancy due to diverse and uncontrollable shape representation cross datasets.
Object-Guided Instance Segmentation With Auxiliary Feature Refinement for Biological Images
To deal with this problem, in this paper, we propose an object-guided instance segmentation method.
On Universal Black-Box Domain Adaptation
The great promise that UB$^2$DA makes, however, brings significant learning challenges, since domain adaptation can only rely on the predictions of unlabeled target data in a partially overlapped label space, by accessing the interface of source model.
Generation of bottle beam using low-density channel in air
Cylindrical density depressions generated by femtosecond laser pulses filamenting in air for different energy depositions is investigated numerically, by using a set of hydrodynamic equations.
Optics Plasma Physics
Vicinal and categorical domain adaptation
Unsupervised domain adaptation aims to learn a task classifier that performs well on the unlabeled target domain, by utilizing the labeled source domain.
Geometry-Aware Self-Training for Unsupervised Domain Adaptation on Object Point Clouds
The point cloud representation of an object can have a large geometric variation in view of inconsistent data acquisition procedure, which thus leads to domain discrepancy due to diverse and uncontrollable shape representation cross datasets.
Towards Uncovering the Intrinsic Data Structures for Unsupervised Domain Adaptation using Structurally Regularized Deep Clustering
To address this issue, we are motivated by a UDA assumption of structural similarity across domains, and propose to directly uncover the intrinsic target discrimination via constrained clustering, where we constrain the clustering solutions using structural source regularization that hinges on the very same assumption.
Bipartite Distance for Shape-Aware Landmark Detection in Spinal X-Ray Images
Scoliosis is a congenital disease that causes lateral curvature in the spine.
Partly Supervised Multitask Learning
Leveraging self-supervision and adversarial training, we propose a novel general purpose semi-supervised, multiple-task model---namely, self-supervised, semi-supervised, multitask learning (S$^4$MTL)---for accomplishing two important tasks in medical imaging, segmentation and diagnostic classification.
Analysis of Scoliosis From Spinal X-Ray Images
Leveraging a carefully-adjusted U-Net model with progressive side outputs, we propose an end-to-end segmentation model that provides a fully automatic and reliable segmentation of the vertebrae associated with scoliosis measurement.
Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering
To alleviate this risk, we are motivated by the assumption of structural domain similarity, and propose to directly uncover the intrinsic target discrimination via discriminative clustering of target data.
Unsupervised Multi-Class Domain Adaptation: Theory, Algorithms, and Practice
By using MCSD as a measure of domain distance, we develop a new domain adaptation bound for multi-class UDA; its data-dependent, probably approximately correct bound is also developed that naturally suggests adversarial learning objectives to align conditional feature distributions across source and target domains.
Shape-Aware Organ Segmentation by Predicting Signed Distance Maps
In this work, we propose to resolve the issue existing in current deep learning based organ segmentation systems that they often produce results that do not capture the overall shape of the target organ and often lack smoothness.
Discriminative Adversarial Domain Adaptation
Based on an integrated category and domain classifier, DADA has a novel adversarial objective that encourages a mutually inhibitory relation between category and domain predictions for any input instance.
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Object-Guided Instance Segmentation for Biological Images
Along with the instance normalization, the model is able to recover the target object distribution and suppress the distribution of neighboring attached objects.
Domain-Symmetric Networks for Adversarial Domain Adaptation
Since target samples are unlabeled, we also propose a scheme of cross-domain training to help learn the target classifier.
Generating Realistic Training Images Based on Tonality-Alignment Generative Adversarial Networks for Hand Pose Estimation
Hand pose estimation from a monocular RGB image is an important but challenging task.
3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes
In this paper, we propose a network architecture and the corresponding loss function which improve segmentation of very small structures.
Fine-Grained Visual Categorization using Meta-Learning Optimization with Sample Selection of Auxiliary Data
Fine-grained visual categorization (FGVC) is challenging due in part to the fact that it is often difficult to acquire an enough number of training samples.
