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Found 10 papers, 0 papers with code
Domain Generalization Guided by Gradient Signal to Noise Ratio of Parameters
Overfitting to the source domain is a common issue in gradient-based training of deep neural networks.
Controllable Dynamic Multi-Task Architectures
In contrast to the existing dynamic multi-task approaches that adjust only the weights within a fixed architecture, our approach affords the flexibility to dynamically control the total computational cost and match the user-preferred task importance better.
On Generalizing Beyond Domains in Cross-Domain Continual Learning
Humans have the ability to accumulate knowledge of new tasks in varying conditions, but deep neural networks often suffer from catastrophic forgetting of previously learned knowledge after learning a new task.
Learning Semantic Segmentation from Multiple Datasets with Label Shifts
First, a gradient conflict in training due to mismatched label spaces is identified and a class-independent binary cross-entropy loss is proposed to alleviate such label conflicts.
Learning to Learn across Diverse Data Biases in Deep Face Recognition
Convolutional Neural Networks have achieved remarkable success in face recognition, in part due to the abundant availability of data.
Cross-Domain Similarity Learning for Face Recognition in Unseen Domains
Intuitively, it discriminatively correlates explicit metrics derived from one domain, with triplet samples from another domain in a unified loss function to be minimized within a network, which leads to better alignment of the training domains.
Voting-based Approaches For Differentially Private Federated Learning
Differentially Private Federated Learning (DPFL) is an emerging field with many applications.
Learning Factorized Representations for Open-set Domain Adaptation
To this end, we rely on the intuition that the source and target samples depicting the known classes can be generated by a shared subspace, whereas the target samples from unknown classes come from a different, private subspace.
More About VLAD: A Leap From Euclidean to Riemannian Manifolds
This paper takes a step forward in image and video coding by extending the well-known Vector of Locally Aggregated Descriptors (VLAD) onto an extensive space of curved Riemannian manifolds.
Log-Euclidean Bag of Words for Human Action Recognition
Representing videos by densely extracted local space-time features has recently become a popular approach for analysing actions.
