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Found 13 papers, 4 papers with code
Word-Sequence Entropy: Towards Uncertainty Estimation in Free-Form Medical Question Answering Applications and Beyond
Uncertainty estimation plays a pivotal role in ensuring the reliability of safety-critical human-AI interaction systems, particularly in the medical domain.
ACT-Diffusion: Efficient Adversarial Consistency Training for One-step Diffusion Models
Though diffusion models excel in image generation, their step-by-step denoising leads to slow generation speeds.
Feature Noise Boosts DNN Generalization under Label Noise
In this study, we introduce and theoretically demonstrate a simple feature noise method, which directly adds noise to the features of training data, can enhance the generalization of DNNs under label noise.
Exposing the Fake: Effective Diffusion-Generated Images Detection
Image synthesis has seen significant advancements with the advent of diffusion-based generative models like Denoising Diffusion Probabilistic Models (DDPM) and text-to-image diffusion models.
Caterpillar: A Pure-MLP Architecture with Shifted-Pillars-Concatenation
Then, we build a pure-MLP architecture called Caterpillar by replacing the convolutional layer with the SPC module in a hybrid model of sMLPNet.
An Efficient Membership Inference Attack for the Diffusion Model by Proximal Initialization
Therefore, we also explore the robustness of diffusion models to MIA in the text-to-speech (TTS) task, which is an audio generation task.
Improve Video Representation with Temporal Adversarial Augmentation
In this paper, we introduce Temporal Adversarial Augmentation (TA), a novel video augmentation technique that utilizes temporal attention.
Are Diffusion Models Vulnerable to Membership Inference Attacks?
In this paper, we investigate the vulnerability of diffusion models to Membership Inference Attacks (MIAs), a common privacy concern.
Self-paced Resistance Learning against Overfitting on Noisy Labels
They might significantly deteriorate the performance of convolutional neural networks (CNNs), because CNNs are easily overfitted on corrupted labels.
Iterative Attention Mining for Weakly Supervised Thoracic Disease Pattern Localization in Chest X-Rays
Given image labels as the only supervisory signal, we focus on harvesting, or mining, thoracic disease localizations from chest X-ray images.
Recent Advances in the Applications of Convolutional Neural Networks to Medical Image Contour Detection
Then we review their recent applications in medical image analysis and point out limitations, with the goal to light some potential directions in medical image analysis.
Revisiting Graph Construction for Fast Image Segmentation
In this paper, we propose a simple but effective method for fast image segmentation.
SemiContour: A Semi-supervised Learning Approach for Contour Detection
In this paper, we investigate the usage of semi-supervised learning (SSL) to obtain competitive detection accuracy with very limited training data (three labeled images).
