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Found 22 papers, 5 papers with code
WS-YOLO: Weakly Supervised Yolo Network for Surgical Tool Localization in Endoscopic Videos
Being able to automatically detect and track surgical instruments in endoscopic video recordings would allow for many useful applications that could transform different aspects of surgery.
RT-MonoDepth: Real-time Monocular Depth Estimation on Embedded Systems
Depth sensing is a crucial function of unmanned aerial vehicles and autonomous vehicles.
PL-UNeXt: Per-stage Edge Detail and Line Feature Guided Segmentation for Power Line Detection
Power line detection is a critical inspection task for electricity companies and is also useful in avoiding drone obstacles.
Mutual Information Learned Regressor: an Information-theoretic Viewpoint of Training Regression Systems
We first argue that the MSE minimization approach is equivalent to a conditional entropy learning problem, and then propose a mutual information learning formulation for solving regression problems by using a reparameterization technique.
Improving Continual Relation Extraction through Prototypical Contrastive Learning
Continual relation extraction (CRE) aims to extract relations towards the continuous and iterative arrival of new data, of which the major challenge is the catastrophic forgetting of old tasks.
Neural Volumetric Mesh Generator
We first propose a diffusion-based generative model to tackle this problem by generating voxelized shapes with close-to-reality outlines and structures.
Mutual Information Learned Classifiers: an Information-theoretic Viewpoint of Training Deep Learning Classification Systems
Deep learning systems have been reported to acheive state-of-the-art performances in many applications, and one of the keys for achieving this is the existence of well trained classifiers on benchmark datasets which can be used as backbone feature extractors in downstream tasks.
Mutual Information Learned Classifiers: an Information-theoretic Viewpoint of Training Deep Learning Classification Systems
Deep learning systems have been reported to achieve state-of-the-art performances in many applications, and a key is the existence of well trained classifiers on benchmark datasets.
Robust Modeling of Unknown Dynamical Systems via Ensemble Averaged Learning
In the proposed ensemble averaging method, multiple models are independently trained and model predictions are averaged at each time step.
Exploring Gradient Flow Based Saliency for DNN Model Compression
Model pruning aims to reduce the deep neural network (DNN) model size or computational overhead.
A Deep Learning Inference Scheme Based on Pipelined Matrix Multiplication Acceleration Design and Non-uniform Quantization
Matrix multiplication is the bedrock in Deep Learning inference application.
Personalized Retrogress-Resilient Framework for Real-World Medical Federated Learning
To address this problem, we propose a personalized retrogress-resilient framework to produce a superior personalized model for each client.
Deep Neural Network Modeling of Unknown Partial Differential Equations in Nodal Space
Consequently, a trained DNN defines a predictive model for the underlying unknown PDE over structureless grids.
Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising
While considerable multi-task efforts have been made in this direction, a long-standing challenge is how to explicitly model the long-path sequential dependence among audience multi-step conversions for improving the end-to-end conversion.
Socially-Aware Conference Participant Recommendation with Personality Traits
As a result of the importance of academic collaboration at smart conferences, various researchers have utilized recommender systems to generate effective recommendations for participants.
Data-driven learning of non-autonomous systems
To circumvent the difficulty presented by the non-autonomous nature of the system, our method transforms the solution state into piecewise integration of the system over a discrete set of time instances.
Overcoming information reduced data and experimentally uncertain parameters in ptychography with regularized optimization
The overdetermination of the mathematical problem underlying ptychography is reduced by a host of experimentally more desirable settings.
Methods to Recover Unknown Processes in Partial Differential Equations Using Data
Various numerical examples are then presented to demonstrate the performance and properties of the numerical methods.
On generalized residue network for deep learning of unknown dynamical systems
When an existing coarse model is not available, we present numerical strategies for fast creation of coarse models, to be used in conjunction with the generalized ResNet.
Complementary Network with Adaptive Receptive Fields for Melanoma Segmentation
To tackle these issues, we propose a novel complementary network with adaptive receptive filed learning.
Parameterized Synthetic Image Data Set for Fisheye Lens
Based on different projection geometry, a fisheye image can be presented as a parameterized non-rectilinear image.
Deep Learning Framework for Multi-class Breast Cancer Histology Image Classification
In this work, we present a deep learning framework for multi-class breast cancer image classification as our submission to the International Conference on Image Analysis and Recognition (ICIAR) 2018 Grand Challenge on BreAst Cancer Histology images (BACH).
Breast Cancer Histology Image Classification
General Classification
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