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Meta-learning approaches for few-shot learning: A survey of recent advances
Despite its astounding success in learning deeper multi-dimensional data, the performance
of deep learning declines on new unseen tasks mainly due to its focus on same-distribution …
of deep learning declines on new unseen tasks mainly due to its focus on same-distribution …
Few-shot open-set learning for on-device customization of keyword spotting systems
M Rusci, T Tuytelaars - ar** for few-shot fine-grained visual classification
Q Wu, T Song, S Fan, Z Chen, K **, H Zhou - Image and Vision Computing, 2024 - Elsevier
Few-shot fine-grained visual classification aims to identify fine-grained concepts with very
few samples, which is widely used in many fields, such as the classification of different …
few samples, which is widely used in many fields, such as the classification of different …
Task-agnostic open-set prototype for few-shot open-set recognition
In few-shot open-set recognition (FSOSR), a network learns to recognize closed-set samples
with a few support samples while rejecting open-set samples with no class cue. Unlike …
with a few support samples while rejecting open-set samples with no class cue. Unlike …
Joint embedding learning and latent subspace probing for cross-domain few-shot keyword spotting
M Ozay - ICASSP 2024-2024 IEEE International Conference on …, 2024 - ieeexplore.ieee.org
Probing classifiers (PCs) have been employed as one of the notable approaches for
exploring properties of deep neural network (DNN) models in various tasks such as natural …
exploring properties of deep neural network (DNN) models in various tasks such as natural …
Self-Learning for Personalized Keyword Spotting on Ultra-Low-Power Audio Sensors
This paper proposes a self-learning method to incrementally train (fine-tune) a personalized
Keyword Spotting (KWS) model after the deployment on ultra-low power smart audio …
Keyword Spotting (KWS) model after the deployment on ultra-low power smart audio …
Improving small footprint few-shot keyword spotting with supervision on auxiliary data
Few-shot keyword spotting (FS-KWS) models usually require large-scale annotated datasets
to generalize to unseen target keywords. However, existing KWS datasets are limited in …
to generalize to unseen target keywords. However, existing KWS datasets are limited in …
Fully unsupervised training of few-shot keyword spotting
For training a few-shot keyword spotting (FS-KWS) model, a large labeled dataset
containing massive target keywords has known to be essential to generalize to arbitrary …
containing massive target keywords has known to be essential to generalize to arbitrary …
Unlocking Transfer Learning for Open-World Few-Shot Recognition
Few-Shot Open-Set Recognition (FSOSR) targets a critical real-world challenge, aiming to
categorize inputs into known categories, termed closed-set classes, while identifying open …
categorize inputs into known categories, termed closed-set classes, while identifying open …