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Found 18 papers, 9 papers with code
Supervised Domain Adaptation for Recognizing Retinal Diseases from Wide-Field Fundus Images
This paper addresses the emerging task of recognizing multiple retinal diseases from wide-field (WF) and ultra-wide-field (UWF) fundus images.
Neural networks learn to magnify areas near decision boundaries
This holds in deep networks trained on high-dimensional image classification tasks, and even in self-supervised representation learning.
Backdoor Defense via Suppressing Model Shortcuts
Recent studies have demonstrated that deep neural networks (DNNs) are vulnerable to backdoor attacks during the training process.
The Numerical Stability of Hyperbolic Representation Learning
Given the exponential growth of the volume of the ball w. r. t.
SuperLine3D: Self-supervised Line Segmentation and Description for LiDAR Point Cloud
To repetitively extract them as features and perform association between discrete LiDAR frames for registration, we propose the first learning-based feature segmentation and description model for 3D lines in LiDAR point cloud.
Efficient and Systematic Partitioning of Large and Deep Neural Networks for Parallelization
We propose the Flex-Edge Recursive Graph and the Double Recursive Algorithm, successfully limiting our parallelization strategy generation to a linear complexity with a good quality of parallelization strategy.
Progressive Self-Guided Loss for Salient Object Detection
We present a simple yet effective progressive self-guided loss function to facilitate deep learning-based salient object detection (SOD) in images.
A Method of Generating Measurable Panoramic Image for Indoor Mobile Measurement System
This paper designs a technique route to generate high-quality panoramic image with depth information, which involves two critical research hotspots: fusion of LiDAR and image data and image stitching.
A Simple and Efficient Registration of 3D Point Cloud and Image Data for Indoor Mobile Mapping System
Registration of 3D LiDAR point clouds with optical images is critical in the combination of multi-source data.
Learning Efficient Parameter Server Synchronization Policies for Distributed SGD
We apply a reinforcement learning (RL) based approach to learning optimal synchronization policies used for Parameter Server-based distributed training of machine learning models with Stochastic Gradient Descent (SGD).
ClusterVO: Clustering Moving Instances and Estimating Visual Odometry for Self and Surroundings
We present ClusterVO, a stereo Visual Odometry which simultaneously clusters and estimates the motion of both ego and surrounding rigid clusters/objects.
Morphing and Sampling Network for Dense Point Cloud Completion
3D point cloud completion, the task of inferring the complete geometric shape from a partial point cloud, has been attracting attention in the community.
Ranked #8 on
Point Cloud Completion
on ShapeNet
High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learning
First, the framework features a practical hierarchical architecture for decomposing the stochastic gradient into its norm and normalized block gradients, and efficiently quantizes them using a uniform quantizer and a low-dimensional codebook on a Grassmann manifold, respectively.
A Dilated Inception Network for Visual Saliency Prediction
In this work, we proposed an end-to-end dilated inception network (DINet) for visual saliency prediction.
Towards Robust Curve Text Detection with Conditional Spatial Expansion
It is challenging to detect curve texts due to their irregular shapes and varying sizes.
Learning Markov Clustering Networks for Scene Text Detection
MCN predicts instance-level bounding boxes by firstly converting an image into a Stochastic Flow Graph (SFG) and then performing Markov Clustering on this graph.
Flint Water Crisis: Data-Driven Risk Assessment Via Residential Water Testing
In this analysis, we find that lead service lines are not the only factor that is predictive of the risk of lead contamination of water.
Data Science in Service of Performing Arts: Applying Machine Learning to Predicting Audience Preferences
Performing arts organizations aim to enrich their communities through the arts.
