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Found 13 papers, 6 papers with code
Weak-to-Strong 3D Object Detection with X-Ray Distillation
This paper addresses the critical challenges of sparsity and occlusion in LiDAR-based 3D object detection.
Adversarial Attacks and Defenses in Automated Control Systems: A Comprehensive Benchmark
By evaluating three neural networks with different architectures, we subject them to six types of adversarial attacks and explore five different defense methods.
A Semi-Supervised Deep Learning Approach to Dataset Collection for Query-By-Humming Task
To expand our dataset, we employ a semi-supervised model training pipeline that leverages the QbH task as a specialized case of cover song identification (CSI) task.
Refining the ONCE Benchmark with Hyperparameter Tuning
In response to the growing demand for 3D object detection in applications such as autonomous driving, robotics, and augmented reality, this work focuses on the evaluation of semi-supervised learning approaches for point cloud data.
Interaction models for remaining useful life estimation
The paper deals with the problem of controlling the state of industrial devices according to the readings of their sensors.
Exploring Efficiency of Vision Transformers for Self-Supervised Monocular Depth Estimation
Depth estimation is a crucial task for the creation of depth maps, one of the most important components for augmented reality (AR) and other applications.
Graph Neural Networks with Trainable Adjacency Matrices for Fault Diagnosis on Multivariate Sensor Data
In this work, the possibility of applying graph neural networks to the problem of fault diagnosis in a chemical process is studied.
Long-term hail risk assessment with deep neural networks
But there are no machine learning models for data-driven forecasting of changes in hail frequency for a given area.
SensorSCAN: Self-Supervised Learning and Deep Clustering for Fault Diagnosis in Chemical Processes
However, manual annotation of large amounts of data can be difficult in industrial settings.
Dealing with Sparse Rewards Using Graph Neural Networks
Deep reinforcement learning in partially observable environments is a difficult task in itself, and can be further complicated by a sparse reward signal.
Temporal Graph Network Embedding with Causal Anonymous Walks Representations
For evaluation, we provide a benchmark pipeline for the evaluation of temporal network embeddings.
Hotel Recognition via Latent Image Embedding
We approach the problem of hotel recognition with deep metric learning.
Epidemic modelling of multiple virus strains: a case study of SARS-CoV-2 B.1.1.7 in Moscow
During a long-running pandemic a pathogen can mutate, producing new strains with different epidemiological parameters.
