Search Results for author:
Found 18 papers, 5 papers with code
Variational Label Enhancement
Label distribution covers a certain number of labels, representing the degree to which each label describes the instance.
Are Dense Labels Always Necessary for 3D Object Detection from Point Cloud?
Current state-of-the-art (SOTA) 3D object detection methods often require a large amount of 3D bounding box annotations for training.
Dual Meta-Learning with Longitudinally Generalized Regularization for One-Shot Brain Tissue Segmentation Across the Human Lifespan
However, segmentation on longitudinal data is challenging due to dynamic brain changes across the lifespan.
DAC-MR: Data Augmentation Consistency Based Meta-Regularization for Meta-Learning
Besides, meta-data-driven meta-loss objective combined with DAC-MR is capable of achieving better meta-level generalization.
Improve Noise Tolerance of Robust Loss via Noise-Awareness
To achieve setting such instance-dependent hyperparameters for robust loss, we propose a meta-learning method capable of adaptively learning a hyperparameter prediction function, called Noise-Aware-Robust-Loss-Adjuster (NARL-Adjuster).
Dual Meta-Learning with Longitudinally Consistent Regularization for One-Shot Brain Tissue Segmentation Across the Human Lifespan
However, segmentation on longitudinal data is challenging due to dynamic brain changes across the lifespan.
A Hyper-weight Network for Hyperspectral Image Denoising
Extensive experiments verify that the proposed HWnet can help improve the generalization ability of a weighted model to adapt to more complex noise, and can also strengthen the weighted model by transferring the knowledge from another weighted model.
Diagnosing Batch Normalization in Class Incremental Learning
Extensive researches have applied deep neural networks (DNNs) in class incremental learning (Class-IL).
CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning
Specifically, by seeing each training class as a separate learning task, our method aims to extract an explicit weighting function with sample loss and task/class feature as input, and sample weight as output, expecting to impose adaptively varying weighting schemes to different sample classes based on their own intrinsic bias characteristics.
Ranked #3 on
Image Classification
on WebVision-1000
Learning an Explicit Hyperparameter Prediction Function Conditioned on Tasks
Meta learning has attracted much attention recently in machine learning community.
Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction
Current machine learning has made great progress on computer vision and many other fields attributed to the large amount of high-quality training samples, while it does not work very well on genomic data analysis, since they are notoriously known as small data.
Meta Feature Modulator for Long-tailed Recognition
We further design a modulator network to guide the generation of the modulation parameters, and such a meta-learner can be readily adapted to train the classification network on other long-tailed datasets.
Learning to Purify Noisy Labels via Meta Soft Label Corrector
By viewing the label correction procedure as a meta-process and using a meta-learner to automatically correct labels, we could adaptively obtain rectified soft labels iteratively according to current training problems without manually preset hyper-parameters.
MLR-SNet: Transferable LR Schedules for Heterogeneous Tasks
Meanwhile, it always needs to search proper LR schedules from scratch for new tasks, which, however, are often largely different with task variations, like data modalities, network architectures, or training data capacities.
Meta Transition Adaptation for Robust Deep Learning with Noisy Labels
To discover intrinsic inter-class transition probabilities underlying data, learning with noise transition has become an important approach for robust deep learning on corrupted labels.
Learning Adaptive Loss for Robust Learning with Noisy Labels
Four kinds of SOTA robust loss functions are attempted to be integrated into our algorithm, and comprehensive experiments substantiate the general availability and effectiveness of the proposed method in both its accuracy and generalization performance, as compared with conventional hyperparameter tuning strategy, even with carefully tuned hyperparameters.
Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting
Current deep neural networks (DNNs) can easily overfit to biased training data with corrupted labels or class imbalance.
Ranked #24 on
Image Classification
on Clothing1M
(using extra training data)
Small Sample Learning in Big Data Era
This category mainly focuses on learning with insufficient samples, and can also be called small data learning in some literatures.
