38,676 research outputs found
Self Meta Pseudo Labels: Meta Pseudo Labels Without The Teacher
We present Self Meta Pseudo Labels, a novel semi-supervised learning method
similar to Meta Pseudo Labels but without the teacher model. We introduce a
novel way to use a single model for both generating pseudo labels and
classification, allowing us to store only one model in memory instead of two.
Our method attains similar performance to the Meta Pseudo Labels method while
drastically reducing memory usage.Comment: Accepted by IEEE ICMLA 202
Learning Variational Neighbor Labels for Test-Time Domain Generalization
This paper strives for domain generalization, where models are trained
exclusively on source domains before being deployed at unseen target domains.
We follow the strict separation of source training and target testing but
exploit the value of the unlabeled target data itself during inference. We make
three contributions. First, we propose probabilistic pseudo-labeling of target
samples to generalize the source-trained model to the target domain at test
time. We formulate the generalization at test time as a variational inference
problem by modeling pseudo labels as distributions to consider the uncertainty
during generalization and alleviate the misleading signal of inaccurate pseudo
labels. Second, we learn variational neighbor labels that incorporate the
information of neighboring target samples to generate more robust pseudo
labels. Third, to learn the ability to incorporate more representative target
information and generate more precise and robust variational neighbor labels,
we introduce a meta-generalization stage during training to simulate the
generalization procedure. Experiments on six widely-used datasets demonstrate
the benefits, abilities, and effectiveness of our proposal.Comment: Under revie
Pseudo-Labeling Based Practical Semi-Supervised Meta-Training for Few-Shot Learning
Most existing few-shot learning (FSL) methods require a large amount of
labeled data in meta-training, which is a major limit. To reduce the
requirement of labels, a semi-supervised meta-training setting has been
proposed for FSL, which includes only a few labeled samples and numbers of
unlabeled samples in base classes. However, existing methods under this setting
require class-aware sample selection from the unlabeled set, which violates the
assumption of unlabeled set. In this paper, we propose a practical
semi-supervised meta-training setting with truly unlabeled data. Under the new
setting, the performance of existing methods drops notably. To better utilize
both the labeled and truly unlabeled data, we propose a simple and effective
meta-training framework, called pseudo-labeling based on meta-learning (PLML).
Firstly, we train a classifier via common semi-supervised learning (SSL) and
use it to obtain the pseudo-labels of unlabeled data. Then we build few-shot
tasks from labeled and pseudo-labeled data and run meta-learning over the
constructed tasks to learn the FSL model. Surprisingly, through extensive
experiments across two FSL datasets, we find that this simple meta-training
framework effectively prevents the performance degradation of FSL under limited
labeled data. Besides, benefiting from meta-training, the proposed method
improves the classifiers learned by two representative SSL algorithms as well
MetaASSIST: Robust Dialogue State Tracking with Meta Learning
Existing dialogue datasets contain lots of noise in their state annotations. Such noise can hurt model training and ultimately lead to poor generalization performance. A general framework named ASSIST has recently been proposed to train robust dialogue state tracking (DST) models. It introduces an auxiliary model to generate pseudo labels for the noisy training set. These pseudo labels are combined with vanilla labels by a common fixed weighting parameter to train the primary DST model. Notwithstanding the improvements of ASSIST on DST, tuning the weighting parameter is challenging. Moreover, a single parameter shared by all slots and all instances may be suboptimal. To overcome these limitations, we propose a meta learning-based framework MetaASSIST to adaptively learn the weighting parameter. Specifically, we propose three schemes with varying degrees of flexibility, ranging from slot-wise to both slot-wise and instance-wise, to convert the weighting parameter into learnable functions. These functions are trained in a meta-learning manner by taking the validation set as meta data. Experimental results demonstrate that all three schemes can achieve competitive performance. Most impressively, we achieve a state-of-the-art joint goal accuracy of 80.10% on MultiWOZ 2.4
SRoUDA: Meta Self-training for Robust Unsupervised Domain Adaptation
As acquiring manual labels on data could be costly, unsupervised domain
adaptation (UDA), which transfers knowledge learned from a rich-label dataset
to the unlabeled target dataset, is gaining increasing popularity. While
extensive studies have been devoted to improving the model accuracy on target
domain, an important issue of model robustness is neglected. To make things
worse, conventional adversarial training (AT) methods for improving model
robustness are inapplicable under UDA scenario since they train models on
adversarial examples that are generated by supervised loss function. In this
paper, we present a new meta self-training pipeline, named SRoUDA, for
improving adversarial robustness of UDA models. Based on self-training
paradigm, SRoUDA starts with pre-training a source model by applying UDA
baseline on source labeled data and taraget unlabeled data with a developed
random masked augmentation (RMA), and then alternates between adversarial
target model training on pseudo-labeled target data and finetuning source model
by a meta step. While self-training allows the direct incorporation of AT in
UDA, the meta step in SRoUDA further helps in mitigating error propagation from
noisy pseudo labels. Extensive experiments on various benchmark datasets
demonstrate the state-of-the-art performance of SRoUDA where it achieves
significant model robustness improvement without harming clean accuracy. Code
is available at https://github.com/Vision.Comment: This paper has been accepted for presentation at the AAAI202
- …