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Found 9 papers, 4 papers with code
Teach Me How to Denoise: A Universal Framework for Denoising Multi-modal Recommender Systems via Guided Calibration
However, MMRecs struggle with noisy data caused by misalignment among modal content and the gap between modal semantics and recommendation semantics.
Uncertainty-aware Knowledge Tracing
Previous research commonly adopts deterministic representation to capture students' knowledge states, which neglects the uncertainty during student interactions and thus fails to model the true knowledge state in learning process.
An Efficient Framework for Enhancing Discriminative Models via Diffusion Techniques
Image classification serves as the cornerstone of computer vision, traditionally achieved through discriminative models based on deep neural networks.
Noise Diffusion for Enhancing Semantic Faithfulness in Text-to-Image Synthesis
Diffusion models have achieved impressive success in generating photorealistic images, but challenges remain in ensuring precise semantic alignment with input prompts.
AdvLogo: Adversarial Patch Attack against Object Detectors based on Diffusion Models
With the rapid development of deep learning, object detectors have demonstrated impressive performance; however, vulnerabilities still exist in certain scenarios.
Boosting Single Positive Multi-label Classification with Generalized Robust Loss
Multi-label learning (MLL) requires comprehensive multi-semantic annotations that is hard to fully obtain, thus often resulting in missing labels scenarios.
A Reinforcement Learning based Reset Policy for CDCL SAT Solvers
In most solvers, variable activities are preserved across restart boundaries, resulting in solvers continuing to search parts of the assignment tree that are not far from the one immediately prior to a restart.
Boosting Multi-modal Model Performance with Adaptive Gradient Modulation
In addition, we find that the jointly trained model typically has a preferred modality on which the competition is weaker than other modalities.
Self Supervised Lesion Recognition For Breast Ultrasound Diagnosis
Since an ultrasound image only describes a partial 2D projection of a 3D lesion, such paradigm ignores the semantic relationship between different views of a lesion, which is inconsistent with the traditional diagnosis where sonographers analyze a lesion from at least two views.
