Search Results for author:
Found 7 papers, 1 papers with code
Inspecting Explainability of Transformer Models with Additional Statistical Information
Transformer becomes more popular in the vision domain in recent years so there is a need for finding an effective way to interpret the Transformer model by visualizing it.
The Effects of Mixed Sample Data Augmentation are Class Dependent
Mixed Sample Data Augmentation (MSDA) techniques, such as Mixup, CutMix, and PuzzleMix, have been widely acknowledged for enhancing performance in a variety of tasks.
Reinforcement Learning-Based Black-Box Model Inversion Attacks
Model inversion attacks are a type of privacy attack that reconstructs private data used to train a machine learning model, solely by accessing the model.
Test-Time Mixup Augmentation for Data and Class-Specific Uncertainty Estimation in Deep Learning Image Classification
Our experiments show that (1) TTMA-DU more effectively differentiates correct and incorrect predictions compared to existing uncertainty measures due to mixup perturbation, and (2) TTMA-CSU provides information on class confusion and class similarity for both datasets.
Noisy Label Classification using Label Noise Selection with Test-Time Augmentation Cross-Entropy and NoiseMix Learning
In the classifier learning, we propose the NoiseMix method based on MixUp and BalancedMix methods by mixing the samples from the noisy and the clean label data.
Progressive Seed Generation Auto-encoder for Unsupervised Point Cloud Learning
With the development of 3D scanning technologies, 3D vision tasks have become a popular research area.
Ranked #8 on
3D Point Cloud Linear Classification
on ModelNet40
3D Point Cloud Linear Classification
Point cloud reconstruction
PBP-Net: Point Projection and Back-Projection Network for 3D Point Cloud Segmentation
In this paper, we propose a simple and efficient architecture named point projection and back-projection network (PBP-Net), which leverages 2D CNNs for the 3D point cloud segmentation.
