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Found 9 papers, 3 papers with code
Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoders
The significant divergence of time series data across different domains presents two primary challenges in building such a general model: (1) meeting the diverse requirements of appropriate information bottlenecks tailored to different datasets in one unified model, and (2) enabling distinguishment between multiple normal and abnormal patterns, both are crucial for effective anomaly detection in various target scenarios.
ROSE: Register Assisted General Time Series Forecasting with Decomposed Frequency Learning
Enabling general time series forecasting faces two challenges: how to obtain unified representations from multi-domian time series data, and how to capture domain-specific features from time series data across various domains for adaptive transfer in downstream tasks.
Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting
Multi-scale division divides the time series into different temporal resolutions using patches of various sizes.
CLIPood: Generalizing CLIP to Out-of-Distributions
This paper aims at generalizing CLIP to out-of-distribution test data on downstream tasks.
Hub-Pathway: Transfer Learning from A Hub of Pre-trained Models
The proposed framework can be trained end-to-end with the target task-specific loss, where it learns to explore better pathway configurations and exploit the knowledge in pre-trained models for each target datum.
Transferability in Deep Learning: A Survey
The success of deep learning algorithms generally depends on large-scale data, while humans appear to have inherent ability of knowledge transfer, by recognizing and applying relevant knowledge from previous learning experiences when encountering and solving unseen tasks.
Omni-Training: Bridging Pre-Training and Meta-Training for Few-Shot Learning
While pre-training and meta-training can create deep models powerful for few-shot generalization, we find that pre-training and meta-training focuses respectively on cross-domain transferability and cross-task transferability, which restricts their data efficiency in the entangled settings of domain shift and task shift.
Zoo-Tuning: Adaptive Transfer from a Zoo of Models
We propose \emph{Zoo-Tuning} to address these challenges, which learns to adaptively transfer the parameters of pretrained models to the target task.
Open Domain Generalization with Domain-Augmented Meta-Learning
Leveraging datasets available to learn a model with high generalization ability to unseen domains is important for computer vision, especially when the unseen domain's annotated data are unavailable.
