Metadiff: Meta-learning with conditional diffusion for few-shot learning
Equipping a deep model the ability of few-shot learning (FSL) is a core challenge for artificial
intelligence. Gradient-based meta-learning effectively addresses the challenge by learning …
intelligence. Gradient-based meta-learning effectively addresses the challenge by learning …
Few-shot intent detection with self-supervised pretraining and prototype-aware attention
S Yang, YJ Du, X Zheng, XY Li, XL Chen, YL Li… - Pattern Recognition, 2024 - Elsevier
Few-shot intent detection is a more challenging application. However, traditional prototypical
networks based on averaging often suffer from issues such as missing key information, poor …
networks based on averaging often suffer from issues such as missing key information, poor …
Few-shot multi-domain text intent classification with Dynamic Balance Domain Adaptation Meta-learning
User intents are ever-changing, which requires deep learning models to have the ability to
classify unknown intents. Meta-learning aims to solve this problem by improving the model's …
classify unknown intents. Meta-learning aims to solve this problem by improving the model's …
Few-shot cyberviolence intent classification with Meta-learning AutoEncoder based on adversarial domain adaptation
S Yang, YJ Du, SY Du, XY Li, XL Chen, YL Li, CZ Xie… - Neurocomputing, 2024 - Elsevier
The phenomenon of cyberviolence has become a critical issue in online security, drawing
attention from various stakeholders. A major shortcoming in the previous works is the …
attention from various stakeholders. A major shortcoming in the previous works is the …
Generative Probabilistic Meta-Learning for Few-Shot Image Classification
M Fu, X Wang, J Wang, Z Yi - IEEE Transactions on Emerging …, 2024 - ieeexplore.ieee.org
Meta-learning, a rapidly advancing area in computational intelligence, leverages prior
knowledge from related tasks to facilitate the swift adaptation to new tasks with limited data …
knowledge from related tasks to facilitate the swift adaptation to new tasks with limited data …
Few-shot intent detection with mutual information and contrastive learning
S Yang, YJ Du, JM Huang, XY Li, SY Du, J Liu… - Applied Soft …, 2024 - Elsevier
Few-shot intent detection is a challenging task. Most existing methods only focus on
acquisition of generalization knowledge in known classes, or on the adaptation situation of …
acquisition of generalization knowledge in known classes, or on the adaptation situation of …
Meta-Learning With Versatile Loss Geometries for Fast Adaptation Using Mirror Descent
Utilizing task-invariant prior knowledge extracted from related tasks, meta-learning is a
principled framework that empowers learning a new task especially when data records are …
principled framework that empowers learning a new task especially when data records are …
Meta-Learning Priors Using Unrolled Proximal Networks
Y Zhang, GB Giannakis - The Twelfth International Conference on Learning … - openreview.net
Relying on prior knowledge accumulated from related tasks, meta-learning offers a powerful
approach to learning a novel task from a limited number of training data. Recent approaches …
approach to learning a novel task from a limited number of training data. Recent approaches …
Meta-Learning Universal Priors Using Non-Injective Change of Variables
Meta-learning empowers data-hungry deep neural networks to rapidly learn from merely a
few samples, which is especially appealing to tasks with small datasets. Critical in this …
few samples, which is especially appealing to tasks with small datasets. Critical in this …
Meta-Learning Universal Priors Using Non-Injective Normalizing Flows
Y Zhang, A Sadeghi, GB Giannakis - openreview.net
Meta-learning empowers data-hungry deep neural networks to rapidly learn from merely a
few samples, which is especially appealing to tasks with small datasets. Critical in this …
few samples, which is especially appealing to tasks with small datasets. Critical in this …