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Found 6 papers, 1 papers with code
BiasAdv: Bias-Adversarial Augmentation for Model Debiasing
Our key idea is that an adversarial attack on a biased model that makes decisions based on spurious correlations may generate synthetic bias-conflicting samples, which can then be used as augmented training data for learning a debiased model.
Sample-wise Label Confidence Incorporation for Learning with Noisy Labels
Although recent studies on designing a robust objective function to label noise, known as the robust loss method, have shown promising results for learning with noisy labels, they suffer from the issue of underfitting not only noisy samples but also clean ones, leading to suboptimal model performance.
Growing a Brain with Sparsity-Inducing Generation for Continual Learning
The base network learns knowledge of sequential tasks, and the sparsity-inducing hypernetwork generates parameters for each time step for evolving old knowledge.
Deep Elastic Networks with Model Selection for Multi-Task Learning
To this end, we propose an efficient approach to exploit a compact but accurate model in a backbone architecture for each instance of all tasks.
Deep Virtual Networks for Memory Efficient Inference of Multiple Tasks
To this end, we propose a novel network architecture producing multiple networks of different configurations, termed deep virtual networks (DVNs), for different tasks.
NestedNet: Learning Nested Sparse Structures in Deep Neural Networks
A nested sparse network consists of multiple levels of networks with a different sparsity ratio associated with each level, and higher level networks share parameters with lower level networks to enable stable nested learning.
