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Found 23 papers, 3 papers with code
Data-oriented Dynamic Fine-tuning Parameter Selection Strategy for FISH Mask based Efficient Fine-tuning
However, there are few methods that consider the impact of data samples on parameter selecting, such as Fish Mask based method.
Rich Semantic Knowledge Enhanced Large Language Models for Few-shot Chinese Spell Checking
We found that by introducing a small number of specific Chinese rich semantic structures, LLMs achieve better performance than the BERT-based model on few-shot CSC task.
Joint Learning for Scattered Point Cloud Understanding with Hierarchical Self-Distillation
Numerous point-cloud understanding techniques focus on whole entities and have succeeded in obtaining satisfactory results and limited sparsity tolerance.
Modality-Agnostic Learning for Medical Image Segmentation Using Multi-modality Self-distillation
Medical image segmentation of tumors and organs at risk is a time-consuming yet critical process in the clinic that utilizes multi-modality imaging (e. g, different acquisitions, data types, and sequences) to increase segmentation precision.
Dual self-distillation of U-shaped networks for 3D medical image segmentation
U-shaped networks and its variants have demonstrated exceptional results for medical image segmentation.
Spatial-Temporal Synchronous Graph Transformer network (STSGT) for COVID-19 forecasting
COVID-19 has become a matter of serious concern over the last few years.
Automated Identification of Toxic Code Reviews Using ToxiCR
ToxiCR includes a choice to select one of the ten supervised learning algorithms, an option to select text vectorization techniques, eight preprocessing steps, and a large-scale labeled dataset of 19, 571 code review comments.
Learning Pruned Structure and Weights Simultaneously from Scratch: an Attention based Approach
As a deep learning model typically contains millions of trainable weights, there has been a growing demand for a more efficient network structure with reduced storage space and improved run-time efficiency.
"Zero-Shot" Point Cloud Upsampling
Recent supervised point cloud upsampling methods are restricted by the size of training data and are limited in terms of covering all object shapes.
SA-GAN: Structure-Aware GAN for Organ-Preserving Synthetic CT Generation
In medical image synthesis, model training could be challenging due to the inconsistencies between images of different modalities even with the same patient, typically caused by internal status/tissue changes as different modalities are usually obtained at a different time.
FREA-Unet: Frequency-aware U-net for Modality Transfer
While Positron emission tomography (PET) imaging has been widely used in diagnosis of number of diseases, it has costly acquisition process which involves radiation exposure to patients.
Objective Class-based Micro-Expression Recognition through Simultaneous Action Unit Detection and Feature Aggregation
Specifically, we propose two new strategies in our AU detection module for more effective AU feature learning: the attention mechanism and the balanced detection loss function.
Generating Long-term Continuous Multi-type Generation Profiles
Traditional long-term planning studies that most utility companies perform based on discrete power levels such as peak or average values cannot reflect system dynamics and often fail to accurately predict system reliability deficiencies.
Attention-Guided Generative Adversarial Network to Address Atypical Anatomy in Modality Transfer
Recently, interest in MR-only treatment planning using synthetic CTs (synCTs) has grown rapidly in radiation therapy.
SPA-GAN: Spatial Attention GAN for Image-to-Image Translation
Image-to-image translation is to learn a mapping between images from a source domain and images from a target domain.
Generative Adversarial Network
Image-to-Image Translation
+2
Multi-year Long-term Load Forecast for Area Distribution Feeders based on Selective Sequence Learning
It was compared with traditional methods and our previous sequence prediction method.
A Pattern Recognition Method for Partial Discharge Detection on Insulated Overhead Conductors
Today, insulated overhead conductors are increasingly used in many places of the world due to the higher operational reliability, elimination of phase-to-phase contact, closer distances between phases and stronger protection for animals.
Combining Unsupervised and Supervised Learning for Asset Class Failure Prediction in Power Systems
To solve this important problem, this paper proposes a novel and comprehensive data-driven approach based on asset condition data: K-means clustering as an unsupervised learning method is used to analyze the inner structure of historical asset condition data and produce the asset conditional ages; logistic regression as a supervised learning method takes in both asset physical ages and conditional ages to classify and predict asset statuses.
A Hybrid Distribution Feeder Long-Term Load Forecasting Method Based on Sequence Prediction
The goal of this task is to forecast the annual load of distribution feeders.
Coupled End-to-End Transfer Learning With Generalized Fisher Information
In transfer learning, one seeks to transfer related information from source tasks with sufficient data to help with the learning of target task with only limited data.
A Data-Driven Residential Transformer Overloading Risk Assessment Method
Residential transformer population is a critical type of asset that many electric utility companies have been attempting to manage proactively and effectively to reduce unexpected failures and life losses that are often caused by transformer overloading.
Directionally Convolutional Networks for 3D Shape Segmentation
Previous approaches on 3D shape segmentation mostly rely on heuristic processing and hand-tuned geometric descriptors.
Using Ranking-CNN for Age Estimation
Human age is considered an important biometric trait for human identification or search.
