IEEE transactions on pattern analysis and machine intelligence
Nov 7, 2022
We propose a novel unified frameork for automated distributed active learning (AutoDAL) to address multiple challenging problems in active learning such as limited labeled data, imbalanced datasets, automatic hyperparameter selection as well as scala...
IEEE transactions on pattern analysis and machine intelligence
Nov 7, 2022
We introduce a novel neural network-based BRDF model and a Bayesian framework for object inverse rendering, i.e., joint estimation of reflectance and natural illumination from a single image of an object of known geometry. The BRDF is expressed with ...
IEEE transactions on pattern analysis and machine intelligence
Nov 7, 2022
Correspondence analysis (CA) is a multivariate statistical tool used to visualize and interpret data dependencies by finding maximally correlated embeddings of pairs of random variables. CA has found applications in fields ranging from epidemiology t...
IEEE transactions on pattern analysis and machine intelligence
Nov 7, 2022
Available data in machine learning applications is becoming increasingly complex, due to higher dimensionality and difficult classes. There exists a wide variety of approaches to measuring complexity of labeled data, according to class overlap, separ...
IEEE transactions on pattern analysis and machine intelligence
Nov 7, 2022
Fighting against the pandemic diseases with unique characters requires new sophisticated approaches like the artificial intelligence. This paper develops an artificial intelligence algorithm to produce multi-dimensional policies for controlling and m...
IEEE transactions on pattern analysis and machine intelligence
Nov 7, 2022
Fine-grained visual classification (FGVC) is much more challenging than traditional classification tasks due to the inherently subtle intra-class object variations. Recent works are mainly part-driven (either explicitly or implicitly), with the assumpt...
IEEE transactions on pattern analysis and machine intelligence
Nov 7, 2022
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...
IEEE transactions on pattern analysis and machine intelligence
Nov 7, 2022
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...
IEEE transactions on pattern analysis and machine intelligence
Nov 7, 2022
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...
IEEE transactions on pattern analysis and machine intelligence
Nov 7, 2022
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...