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Found 18 papers, 11 papers with code
Enhancing Few-shot CLIP with Semantic-Aware Fine-Tuning
Hence, we propose fine-tuning the parameters of the attention pooling layer during the training process to encourage the model to focus on task-specific semantics.
ZooPFL: Exploring Black-box Foundation Models for Personalized Federated Learning
When personalized federated learning (FL) meets large foundation models, new challenges arise from various limitations in resources.
DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalization
We propose DIVERSIFY, a general framework, for OOD detection and generalization on dynamic distributions of time series.
Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learning
With the constructed self-supervised learning task, DDLearn enlarges the data diversity and explores the latent activity properties.
FedCLIP: Fast Generalization and Personalization for CLIP in Federated Learning
Concretely, we design an attention-based adapter for the large model, CLIP, and the rest operations merely depend on adapters.
FIXED: Frustratingly Easy Domain Generalization with Mixup
Firstly, Mixup cannot effectively identify the domain and class information that can be used for learning invariant representations.
Out-of-Distribution Representation Learning for Time Series Classification
Time series classification is an important problem in real world.
Towards Optimization and Model Selection for Domain Generalization: A Mixup-guided Solution
For optimization, we utilize an adapted Mixup to generate an out-of-distribution dataset that can guide the preference direction and optimize with Pareto optimization.
Domain-invariant Feature Exploration for Domain Generalization
Internal invariance means that the features can be learned with a single domain and the features capture intrinsic semantics of data, i. e., the property within a domain, which is agnostic to other domains.
Domain Generalization for Activity Recognition via Adaptive Feature Fusion
To this end, we propose \emph{Adaptive Feature Fusion for Activity Recognition~(AFFAR)}, a domain generalization approach that learns to fuse the domain-invariant and domain-specific representations to improve the model's generalization performance.
MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized Healthcare
Federated learning has attracted increasing attention to building models without accessing the raw user data, especially in healthcare.
Semantic-Discriminative Mixup for Generalizable Sensor-based Cross-domain Activity Recognition
Training on existing data often makes the model biased towards the distribution of the training data, thus the model might perform terribly on test data with different distributions.
Personalized Federated Learning with Adaptive Batchnorm for Healthcare
There is a growing interest in applying machine learning techniques to healthcare.
DIVERSIFY to Generalize: Learning Generalized Representations for Time Series Classification
In this paper, we propose to view the time series classification problem from the distribution perspective.
FedHealth 2: Weighted Federated Transfer Learning via Batch Normalization for Personalized Healthcare
The success of machine learning applications often needs a large quantity of data.
Generalizing to Unseen Domains: A Survey on Domain Generalization
Domain generalization deals with a challenging setting where one or several different but related domain(s) are given, and the goal is to learn a model that can generalize to an unseen test domain.
Cross-domain Activity Recognition via Substructural Optimal Transport
In this paper, we propose substructure-level matching for domain adaptation (SSDA) to better utilize the locality information of activity data for accurate and efficient knowledge transfer.
Strain-Rate-Dependent Deformation Behavior of Ti29Zr24Nb23Hf24 High Entropy Alloys at Elevated and Room Temperature
Such a softening behavior can be related to the interaction of dislocations with short-range clustering.
Materials Science
