Search Results for author:
Found 8 papers, 4 papers with code
DIDLM:A Comprehensive Multi-Sensor Dataset with Infrared Cameras, Depth Cameras, LiDAR, and 4D Millimeter-Wave Radar in Challenging Scenarios for 3D Mapping
In summary, this dataset addresses the problem of data scarcity in special environments, fostering the development of perception and mapping algorithms for extreme conditions.
Towards Plastic and Stable Exemplar-Free Incremental Learning: A Dual-Learner Framework with Cumulative Parameter Averaging
The knowledge from the plastic learner is transferred to the stable learner via cumulative parameter averaging.
Decoupling Learning and Remembering: A Bilevel Memory Framework With Knowledge Projection for Task-Incremental Learning
BMKP decouples the functions of learning and knowledge remembering via a bilevel-memory design: a working memory responsible for adaptively model learning, to ensure plasticity; a long-term memory in charge of enduringly storing the knowledge incorporated within the learned model, to guarantee stability.
Exemplar-free Class Incremental Learning via Discriminative and Comparable One-class Classifiers
DisCOIL follows the basic principle of POC, but it adopts variational auto-encoders (VAE) instead of other well-established one-class classifiers (e. g. deep SVDD), because a trained VAE can not only identify the probability of an input sample belonging to a class but also generate pseudo samples of the class to assist in learning new tasks.
TGRNet: A Table Graph Reconstruction Network for Table Structure Recognition
A table arranging data in rows and columns is a very effective data structure, which has been widely used in business and scientific research.
MTNet: A Multi-Task Neural Network for On-Field Calibration of Low-Cost Air Monitoring Sensors
Despite the popularity of this single-task schema, it may neglect interactions among calibration tasks of different sensors, which encompass underlying information to promote calibration performance.
Multi-Component Graph Convolutional Collaborative Filtering
The interactions of users and items in recommender system could be naturally modeled as a user-item bipartite graph.
RECOME: a New Density-Based Clustering Algorithm Using Relative KNN Kernel Density
Furthermore, we discover that the number of clusters computed by RECOME is a step function of the $\alpha$ parameter with jump discontinuity on a small collection of values.
Databases
