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Found 8 papers, 2 papers with code
A Safe Screening Rule with Bi-level Optimization of $ν$ Support Vector Machine
Support vector machine (SVM) has achieved many successes in machine learning, especially for a small sample problem.
DKiS: Decay weight invertible image steganography with private key
Image steganography, defined as the practice of concealing information within another image, traditionally encounters security challenges when its methods become publicly known or are under attack.
PRIS: Practical robust invertible network for image steganography
Most of the existing image steganography methods have low hiding robustness when the container images affected by distortion.
MSTRIQ: No Reference Image Quality Assessment Based on Swin Transformer with Multi-Stage Fusion
Many Image Quality Assessment(IQA) algorithms have been designed to tackle this problem.
Multi-task nonparallel support vector machine for classification
In addition, the property and sensitivity of the parameter in model are further explored.
Multi-Class Classification from Single-Class Data with Confidences
We show that without any assumptions on the loss functions, models, and optimizers, we can successfully learn a multi-class classifier from only data of a single class with a rigorous consistency guarantee when confidences (i. e., the class-posterior probabilities for all the classes) are available.
Learning from Similarity-Confidence Data
Weakly supervised learning has drawn considerable attention recently to reduce the expensive time and labor consumption of labeling massive data.
Multi-Complementary and Unlabeled Learning for Arbitrary Losses and Models
We first give an unbiased estimator of the classification risk from samples with multiple complementary labels, and then further improve the estimator by incorporating unlabeled samples into the risk formulation.
Ranked #20 on
Image Classification
on Kuzushiji-MNIST
