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of 4 209
pro vyhledávání: '"SOFT-LABELS"'
Automated Facial Expression Recognition (FER) is challenging due to intra-class variations and inter-class similarities. FER can be especially difficult when facial expressions reflect a mixture of various emotions (aka compound expressions). Existin
Externí odkaz:
http://arxiv.org/abs/2410.22506
Autor:
Xiao, Lingao, He, Yang
In ImageNet-condensation, the storage for auxiliary soft labels exceeds that of the condensed dataset by over 30 times. However, are large-scale soft labels necessary for large-scale dataset distillation? In this paper, we first discover that the hig
Externí odkaz:
http://arxiv.org/abs/2410.15919
Autor:
de Vries, Sjoerd, Thierens, Dirk
In supervised machine learning, models are typically trained using data with hard labels, i.e., definite assignments of class membership. This traditional approach, however, does not take the inherent uncertainty in these labels into account. We inve
Externí odkaz:
http://arxiv.org/abs/2409.16071
In recent years, Few-Shot Object Detection (FSOD) has gained widespread attention and made significant progress due to its ability to build models with a good generalization power using extremely limited annotated data. The fine-tuning based paradigm
Externí odkaz:
http://arxiv.org/abs/2408.05674
Estimating a distribution given access to its unnormalized density is pivotal in Bayesian inference, where the posterior is generally known only up to an unknown normalizing constant. Variational inference and Markov chain Monte Carlo methods are the
Externí odkaz:
http://arxiv.org/abs/2407.15687
Autor:
Lu, Yangdi, He, Wenbo
Noisy labels are ubiquitous in real-world datasets, especially in the large-scale ones derived from crowdsourcing and web searching. It is challenging to train deep neural networks with noisy datasets since the networks are prone to overfitting the n
Externí odkaz:
http://arxiv.org/abs/2406.16966
Akademický článek
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Autor:
Jia, Kaidi, Li, Rongsheng
Metaphors play a significant role in our everyday communication, yet detecting them presents a challenge. Traditional methods often struggle with improper application of language rules and a tendency to overlook data sparsity. To address these issues
Externí odkaz:
http://arxiv.org/abs/2403.18253
Classification is a fundamental task in many applications on which data-driven methods have shown outstanding performances. However, it is challenging to determine whether such methods have achieved the optimal performance. This is mainly because the
Externí odkaz:
http://arxiv.org/abs/2401.15500
Publikováno v:
24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), pp. 173-180, 2022. IEEE
Dataset distillation aims at synthesizing a dataset by a small number of artificially generated data items, which, when used as training data, reproduce or approximate a machine learning (ML) model as if it were trained on the entire original dataset
Externí odkaz:
http://arxiv.org/abs/2403.17130