Toward green and human-like artificial intelligence: A complete survey on contemporary few-shot learning approaches
Despite deep learning's widespread success, its data-hungry and computationally
expensive nature makes it impractical for many data-constrained real-world applications …
expensive nature makes it impractical for many data-constrained real-world applications …
C-Disentanglement: discovering causally-independent generative factors under an inductive bias of confounder
Abstract Representation learning assumes that real-world data is generated by a few
semantically meaningful generative factors (ie, sources of variation) and aims to discover …
semantically meaningful generative factors (ie, sources of variation) and aims to discover …
Causal meta-transfer learning for cross-domain few-shot hyperspectral image classification
Y Cheng, W Zhang, H Wang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Few-shot hyperspectral image (HSI) classification poses challenges due to sample selection
bias in few-shot scenarios, potentially leading to incorrect statistical associations between …
bias in few-shot scenarios, potentially leading to incorrect statistical associations between …
Few-shot Classification with Fork Attention Adapter
J Sun, J Li - Pattern Recognition, 2024 - Elsevier
Few-shot learning aims to transfer the knowledge learned from seen categories to unseen
categories with a few references. It is also an essential challenge to bridge the gap between …
categories with a few references. It is also an essential challenge to bridge the gap between …
Towards Causal Relationship in Indefinite Data: Baseline Model and New Datasets
Integrating deep learning and causal discovery has encouraged us to spot that learning
causal structures and representations in dialogue and video is full of challenges. We defined …
causal structures and representations in dialogue and video is full of challenges. We defined …
Prototype Bayesian Meta-Learning for Few-Shot Image Classification
M Fu, X Wang, J Wang, Z Yi - IEEE Transactions on Neural …, 2024 - ieeexplore.ieee.org
Meta-learning aims to leverage prior knowledge from related tasks to enable a base learner
to quickly adapt to new tasks with limited labeled samples. However, traditional meta …
to quickly adapt to new tasks with limited labeled samples. However, traditional meta …
GaitSCM: Causal representation learning for gait recognition
Gait recognition is a promising biometric technology that aims to identify the target subject
via walking pattern. Most existing appearance-based methods focus on learning …
via walking pattern. Most existing appearance-based methods focus on learning …