259 citations · 942 across the 44 of their papers we have counts for
6 papers · 1 filter
Adaptive Poincaré Point to Set Distance for Few-Shot Classification
Rongkai Ma, Pengfei Fang, Tom Drummond +1
Learning and generalizing from limited examples, i,e, few-shot learning, is of core importance to many real-world vision applications. A principal way of achieving few-shot learnin…
Learning Instance and Task-Aware Dynamic Kernels for Few Shot Learning
Rongkai Ma, Pengfei Fang, Gil Avraham +4
Learning and generalizing to novel concepts with few samples (Few-Shot Learning) is still an essential challenge to real-world applications. A principle way of achieving few-shot l…
Dense Uncertainty Estimation via an Ensemble-based Conditional Latent Variable Model
Jing Zhang, Yuchao Dai, Mehrtash Harandi +3
Uncertainty estimation has been extensively studied in recent literature, which can usually be classified as aleatoric uncertainty and epistemic uncertainty. In current aleatoric u…
Semantic-aware Knowledge Distillation for Few-Shot Class-Incremental Learning
Ali Cheraghian, Shafin Rahman, Pengfei Fang +3
Few-shot class incremental learning (FSCIL) portrays the problem of learning new concepts gradually, where only a few examples per concept are available to the learner. Due to the…
Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation
Lie Ju, Xin Wang, Lin Wang +6
Deep neural networks are known to be data-driven and label noise can have a marked impact on model performance. Recent studies have shown great robustness to classic image recognit…
Learning to Continually Learn Rapidly from Few and Noisy Data
Nicholas I-Hsien Kuo, Mehrtash Harandi, Nicolas Fourrier +3
Neural networks suffer from catastrophic forgetting and are unable to sequentially learn new tasks without guaranteed stationarity in data distribution. Continual learning could be…