Search Results for author:
Found 7 papers, 3 papers with code
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.
