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Found 14 papers, 10 papers with code
Towards Macro-AUC oriented Imbalanced Multi-Label Continual Learning
To fill this gap, in this paper, we propose a new memory replay-based method to tackle the imbalance issue for Macro-AUC-oriented MLCL.
Diverse Teacher-Students for Deep Safe Semi-Supervised Learning under Class Mismatch
Semi-supervised learning can significantly boost model performance by leveraging unlabeled data, particularly when labeled data is scarce.
Improving Generalization in Meta-Learning via Meta-Gradient Augmentation
The key idea is to first break the rote memories by network pruning to address memorization overfitting in the inner loop, and then the gradients of pruned sub-networks naturally form the high-quality augmentation of the meta-gradient to alleviate learner overfitting in the outer loop.
MetaViewer: Towards A Unified Multi-View Representation
To overcome them, we propose a novel bi-level-optimization-based multi-view learning framework, where the representation is learned in a uniform-to-specific manner.
Fine-Grained Classification with Noisy Labels
To this end, we propose a novel framework called stochastic noise-tolerated supervised contrastive learning (SNSCL) that confronts label noise by encouraging distinguishable representation.
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Learning with noisy labels
on Food-101
Self-Filtering: A Noise-Aware Sample Selection for Label Noise with Confidence Penalization
Sample selection is an effective strategy to mitigate the effect of label noise in robust learning.
Learning to Rectify for Robust Learning with Noisy Labels
In this paper, we propose warped probabilistic inference (WarPI) to achieve adaptively rectifying the training procedure for the classification network within the meta-learning scenario.
Learning Transferable Parameters for Unsupervised Domain Adaptation
However, the learning processes of domain-invariant features and source hypothesis inevitably involve domain-specific information that would degrade the generalizability of UDA models on the target domain.
Attentional Prototype Inference for Few-Shot Segmentation
We define a global latent variable to represent the prototype of each object category, which we model as a probabilistic distribution.
MetaKernel: Learning Variational Random Features with Limited Labels
Specifically, we propose learning variational random features in a data-driven manner to obtain task-specific kernels by leveraging the shared knowledge provided by related tasks in a meta-learning setting.
Direct Estimation of Spinal Cobb Angles by Structured Multi-Output Regression
The Cobb angle that quantitatively evaluates the spinal curvature plays an important role in the scoliosis diagnosis and treatment.
Learning to Learn Kernels with Variational Random Features
We propose meta variational random features (MetaVRF) to learn adaptive kernels for the base-learner, which is developed in a latent variable model by treating the random feature basis as the latent variable.
DUAL-GLOW: Conditional Flow-Based Generative Model for Modality Transfer
Motivated by developments in modality transfer in vision, we study the generation of certain types of PET images from MRI data.
Learning Deep Match Kernels for Image-Set Classification
Image-set classification has recently generated great popularity due to its widespread applications in computer vision.
