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Found 14 papers, 7 papers with code
Temporal-Spatial Entropy Balancing for Causal Continuous Treatment-Effect Estimation
However, temporal and spatial dimensions are extremely critical in the logistics field, and this limitation may directly affect the precision of subsidy and pricing strategies.
Pseudo Label-Guided Data Fusion and Output Consistency for Semi-Supervised Medical Image Segmentation
Inspired by semi-supervised algorithms that use both labeled and unlabeled data for training, we propose the PLGDF framework, which builds upon the mean teacher network for segmenting medical images with less annotation.
PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification
To address this limitation, we propose an AL-based method that can be simultaneously applied to 2D medical image classification, segmentation, and 3D medical image segmentation tasks.
Contrastive Credibility Propagation for Reliable Semi-Supervised Learning
CCP unifies semi-supervised learning and noisy label learning for the goal of reliably outperforming a supervised baseline in any data scenario.
The Evolutionary Pathways of Disk-, Bulge-, and Halo-dominated Galaxies
In observations, both bulge- and halo-dominated galaxies are likely to be classified as early-type galaxies with compact morphology and quiescent star formation.
Astrophysics of Galaxies
Learning Heatmap-Style Jigsaw Puzzles Provides Good Pretraining for 2D Human Pose Estimation
We note that 2D pose estimation task is highly dependent on the contextual relationship between image patches, thus we introduce a self-supervised method for pretraining 2D pose estimation networks.
A Skew-Sensitive Evaluation Framework for Imbalanced Data Classification
Class distribution skews in imbalanced datasets may lead to models with prediction bias towards majority classes, making fair assessment of classifiers a challenging task.
Free-riders in Federated Learning: Attacks and Defenses
Although the incentive model for federated learning has not been fully developed, it is supposed that participants are able to get rewards or the privilege to use the final global model, as a compensation for taking efforts to train the model.
Time-aware Gradient Attack on Dynamic Network Link Prediction
In this work, we present the first study of adversarial attack on dynamic network link prediction (DNLP).
Robust Anomaly Detection and Backdoor Attack Detection Via Differential Privacy
In this paper, we demonstrate that applying differential privacy can improve the utility of outlier detection and novelty detection, with an extension to detect poisoning samples in backdoor attacks.
Stain Style Transfer using Transitive Adversarial Networks
It is not necessary for an expert to pick a representative reference slide in the proposed TAN method.
Learning Enhanced Resolution-wise features for Human Pose Estimation
Specifically, RAM learns a group of weights to represent the different importance of feature maps across resolutions, and the GPR gradually merges every two feature maps from low to high resolutions to regress final human keypoint heatmaps.
TABOR: A Highly Accurate Approach to Inspecting and Restoring Trojan Backdoors in AI Systems
As such, given a deep neural network model and clean input samples, it is very challenging to inspect and determine the existence of a trojan backdoor.
