Improving Few-Shot Learning Through Multi-task Representation Learning Theory

Bouniot, Quentin; Redko, Ievgen; Audigier, Romaric; Loesch, Angélique; Habrard, Amaury

doi:10.1007/978-3-031-20044-1_25

Cited by 1 publication

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multi-task learning approaches may provide a good representation for few-shot learning [6,11]. [11] proposed a theoretical analysis of learning a common good representation between source and target task with multi-learning, aiming to reveal the maximum extent of sample size reduction.…”

Section: Related Workmentioning

confidence: 99%

“…[11] proposed a theoretical analysis of learning a common good representation between source and target task with multi-learning, aiming to reveal the maximum extent of sample size reduction. [6] explores the framework of multi-task representation (MTR) learning, which aims to leverage source tasks to acquire a representation that reduces the sample complexity of solving a target task. The aforementioned work points out that a representation is crucial for connecting the source and target tasks, but not provides an approach to directly evaluate the benefits of a source task to a target few-shot task.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

TACDFSL: Task Adaptive Cross Domain Few-Shot Learning

2022

View full text Add to dashboard Cite

Cross Domain Few-Shot Learning (CDFSL) has attracted the attention of many scholars since it is closer to reality. The domain shift between the source domain and the target domain is a crucial problem for CDFSL. The essence of domain shift is the marginal distribution difference between two domains which is implicit and unknown. So the empirical marginal distribution measurement is proposed, that is, WDMDS (Wasserstein Distance for Measuring Domain Shift) and MMDMDS (Maximum Mean Discrepancy for Measuring Domain Shift). Besides this, pre-training a feature extractor and fine-tuning a classifier are used in order to have a good generalization in CDFSL. Since the feature obtained by the feature extractor is high-dimensional and left-biased, the adaptive feature distribution transformation is proposed, to make the feature distribution of each sample be approximately Gaussian distribution. This approximate symmetric distribution improves image classification accuracy by 3% on average. In addition, the applicability of different classifiers for CDFSL is investigated, and the classification model should be selected based on the empirical marginal distribution difference between the two domains. The Task Adaptive Cross Domain Few-Shot Learning (TACDFSL) is proposed based on the above ideas. TACDFSL improves image classification accuracy by 3–9%.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%