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Found 7 papers, 3 papers with code
Generalized Category Discovery with Clustering Assignment Consistency
To address the GCD without knowing the class number of unlabeled dataset, we propose a co-training-based framework that encourages clustering consistency.
Tensor Networks Meet Neural Networks: A Survey and Future Perspectives
Interestingly, although these two types of networks originate from different observations, they are inherently linked through the common multilinearity structure underlying both TNs and NNs, thereby motivating a significant number of intellectual developments regarding combinations of TNs and NNs.
Gradient Imitation Reinforcement Learning for General Low-Resource Information Extraction
Low-Resource Information Extraction (LRIE) strives to use unsupervised data, reducing the required resources and human annotation.
AutoFT: Automatic Fine-Tune for Parameters Transfer Learning in Click-Through Rate Prediction
The field-wise transfer policy decides how the pre-trained embedding representations are frozen or fine-tuned based on the given instance from the target domain.
A Survey on Deep Semi-supervised Learning
Deep semi-supervised learning is a fast-growing field with a range of practical applications.
Graph-based Semi-supervised Learning: A Comprehensive Review
An important class of SSL methods is to naturally represent data as graphs such that the label information of unlabelled samples can be inferred from the graphs, which corresponds to graph-based semi-supervised learning (GSSL) methods.
SAR Image Despeckling by Deep Neural Networks: from a pre-trained model to an end-to-end training strategy
Many different schemes have been proposed for the restoration of intensity SAR images.
