AI Medical Compendium Journal:
IEEE transactions on pattern analysis and machine intelligence

Showing 61 to 70 of 300 articles

Fine-Grained Image Analysis With Deep Learning: A Survey.

IEEE transactions on pattern analysis and machine intelligence
Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, and underpins a diverse set of real-world applications. The task of FGIA targets analyzing visual objects from subordinate catego...

Privacy Preserving Defense For Black Box Classifiers Against On-Line Adversarial Attacks.

IEEE transactions on pattern analysis and machine intelligence
Deep learning models have been shown to be vulnerable to adversarial attacks. Adversarial attacks are imperceptible perturbations added to an image such that the deep learning model misclassifies the image with a high confidence. Existing adversarial...

MODENN: A Shallow Broad Neural Network Model Based on Multi-Order Descartes Expansion.

IEEE transactions on pattern analysis and machine intelligence
Deep neural networks have achieved great success in almost every field of artificial intelligence. However, several weaknesses keep bothering researchers due to its hierarchical structure, particularly when large-scale parallelism, faster learning, b...

Ada-LISTA: Learned Solvers Adaptive to Varying Models.

IEEE transactions on pattern analysis and machine intelligence
Neural networks that are based on the unfolding of iterative solvers as LISTA (Learned Iterative Soft Shrinkage), are widely used due to their accelerated performance. These networks, trained with a fixed dictionary, are inapplicable in varying model...

HandVoxNet++: 3D Hand Shape and Pose Estimation Using Voxel-Based Neural Networks.

IEEE transactions on pattern analysis and machine intelligence
3D hand shape and pose estimation from a single depth map is a new and challenging computer vision problem with many applications. Existing methods addressing it directly regress hand meshes via 2D convolutional neural networks, which leads to artifa...

Self-Supervised Human Detection and Segmentation via Background Inpainting.

IEEE transactions on pattern analysis and machine intelligence
While supervised object detection and segmentation methods achieve impressive accuracy, they generalize poorly to images whose appearance significantly differs from the data they have been trained on. To address this when annotating data is prohibiti...

Deep Learning for HDR Imaging: State-of-the-Art and Future Trends.

IEEE transactions on pattern analysis and machine intelligence
High dynamic range (HDR) imaging is a technique that allows an extensive dynamic range of exposures, which is important in image processing, computer graphics, and computer vision. In recent years, there has been a significant advancement in HDR imag...

Learn to Predict Sets Using Feed-Forward Neural Networks.

IEEE transactions on pattern analysis and machine intelligence
This paper addresses the task of set prediction using deep feed-forward neural networks. A set is a collection of elements which is invariant under permutation and the size of a set is not fixed in advance. Many real-world problems, such as image tag...

A Topological Loss Function for Deep-Learning Based Image Segmentation Using Persistent Homology.

IEEE transactions on pattern analysis and machine intelligence
We introduce a method for training neural networks to perform image or volume segmentation in which prior knowledge about the topology of the segmented object can be explicitly provided and then incorporated into the training process. By using the di...

The Emerging Trends of Multi-Label Learning.

IEEE transactions on pattern analysis and machine intelligence
Exabytes of data are generated daily by humans, leading to the growing needs for new efforts in dealing with the grand challenges for multi-label learning brought by big data. For example, extreme multi-label classification is an active and rapidly g...