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Found 6 papers, 3 papers with code
CL-CrossVQA: A Continual Learning Benchmark for Cross-Domain Visual Question Answering
Visual Question Answering (VQA) is a multi-discipline research task.
FRAug: Tackling Federated Learning with Non-IID Features via Representation Augmentation
Real-world applications usually involve a distribution shift across the datasets of the different clients, which hurts the generalization ability of the clients to unseen samples from their respective data distributions.
Towards Data-Free Domain Generalization
In particular, we address the question: How can knowledge contained in models trained on different source domains be merged into a single model that generalizes well to unseen target domains, in the absence of source and target domain data?
Discovery of New Multi-Level Features for Domain Generalization via Knowledge Corruption
Machine learning models that can generalize to unseen domains are essential when applied in real-world scenarios involving strong domain shifts.
ARCADe: A Rapid Continual Anomaly Detector
Although continual learning and anomaly detection have separately been well-studied in previous works, their intersection remains rather unexplored.
Few-Shot One-Class Classification via Meta-Learning
Our experiments on eight datasets from the image and time-series domains show that our method leads to better results than classical OCC and few-shot classification approaches, and demonstrate the ability to learn unseen tasks from only few normal class samples.
