Search Results for author:
Found 11 papers, 3 papers with code
Rethinking Rotation in Self-Supervised Contrastive Learning: Adaptive Positive or Negative Data Augmentation
The semantics of an image can be rotation-invariant or rotation-variant, so whether the rotated image is treated as positive or negative should be determined based on the content of the image.
Generalized Domain Adaptation
The key to our approach is self-supervised class-destructive learning, which enables the learning of class-invariant representations and domain-adversarial classifiers without using any domain labels.
A Novel Perspective for Positive-Unlabeled Learning via Noisy Labels
Positive-unlabeled learning refers to the process of training a binary classifier using only positive and unlabeled data.
The Aleatoric Uncertainty Estimation Using a Separate Formulation with Virtual Residuals
We propose a new optimization framework for aleatoric uncertainty estimation in regression problems.
Multi-Task Curriculum Framework for Open-Set Semi-Supervised Learning
Semi-supervised learning (SSL) has been proposed to leverage unlabeled data for training powerful models when only limited labeled data is available.
Fast and Robust Estimation for Unit-Norm Constrained Linear Fitting Problems
M-estimator using iteratively reweighted least squares (IRLS) is one of the best-known methods for robust estimation.
Local and Global Optimization Techniques in Graph-Based Clustering
We propose a local optimization method, which is widely applicable to graph-based clustering cost functions.
Parallel Grid Pooling for Data Augmentation
Convolutional neural network (CNN) architectures utilize downsampling layers, which restrict the subsequent layers to learn spatially invariant features while reducing computational costs.
Joint Optimization Framework for Learning with Noisy Labels
Deep neural networks (DNNs) trained on large-scale datasets have exhibited significant performance in image classification.
Ranked #39 on
Image Classification
on Clothing1M
Significance of Softmax-based Features in Comparison to Distance Metric Learning-based Features
However, in these DML studies, there were no equitable comparisons between features extracted from a DML-based network and those from a softmax-based network.
Residual Expansion Algorithm: Fast and Effective Optimization for Nonconvex Least Squares Problems
We propose the residual expansion (RE) algorithm: a global (or near-global) optimization method for nonconvex least squares problems.
