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Found 7 papers, 5 papers with code
ADA-GAD: Anomaly-Denoised Autoencoders for Graph Anomaly Detection
We pretrain graph autoencoders on these augmented graphs at multiple levels, which enables the graph autoencoders to capture normal patterns.
Dist-PU: Positive-Unlabeled Learning from a Label Distribution Perspective
Positive-Unlabeled (PU) learning tries to learn binary classifiers from a few labeled positive examples with many unlabeled ones.
MaxMatch: Semi-Supervised Learning with Worst-Case Consistency
In recent years, great progress has been made to incorporate unlabeled data to overcome the inefficiently supervised problem via semi-supervised learning (SSL).
Seeking the Shape of Sound: An Adaptive Framework for Learning Voice-Face Association
Targeting at (a), we propose a two-level modality alignment loss where both global and local information are considered.
Generalized Block-Diagonal Structure Pursuit: Learning Soft Latent Task Assignment against Negative Transfer
Different from most of the previous work, pursuing the Block-Diagonal structure of LTAM (assigning latent tasks to output tasks) alleviates negative transfer via collaboratively grouping latent tasks and output tasks such that inter-group knowledge transfer and sharing is suppressed.
DM2C: Deep Mixed-Modal Clustering
Instead of transforming all the samples into a joint modality-independent space, our framework learns the mappings across individual modal spaces by virtue of cycle-consistency.
Deep Robust Subjective Visual Property Prediction in Crowdsourcing
The problem of estimating subjective visual properties (SVP) of images (e. g., Shoes A is more comfortable than B) is gaining rising attention.
