Although deep learning has made significant progress on fixed large-scale datasets, it typically encounters challenges regarding improperly detecting new/unseen classes in the open-world classification, over-parametrized, and overfitting small samples.
Partial-label (PL) learning is a typical weakly supervised classification problem, where a PL of an instance is a set of candidate labels such that a fixed but unknown candidate is the true label.
During the training process, DLT records the loss value of each sample and calculates dynamic loss thresholds.
In this paper, we present a compact learning (CL) framework to embed the features and labels simultaneously and with mutual guidance.
Partial-label learning (PLL) is a multi-class classification problem, where each training example is associated with a set of candidate labels.
Our method achieves new state-of-the-art results using the single model with 36$\times$(6$\times$) fewer parameters and 2. 6$\times$(2. 1$\times$) faster inference speed on facial age (attractiveness) estimation.
Ranked #1 on Attractiveness Estimation on SCUT-FBP
Partial-label learning (PLL) is a typical weakly supervised learning problem, where each training instance is equipped with a set of candidate labels among which only one is the true label.
To achieve the camera alignment, we develop a Multi-Camera Adversarial Learning (MCAL) to map images of different cameras into a shared subspace.
In this paper, a novel Context-and-Spatial Aware Network (CSANet), which integrates both a Context Aware Path and Spatial Aware Path, is proposed to obtain effective features involving both context information and spatial information.
To solve this problem, we assume that each multi-label instance is described by a vector of latent real-valued labels, which can reflect the importance of the corresponding labels.
However, it is difficult to collect sufficient training images with precise labels in some domains such as apparent age estimation, head pose estimation, multi-label classification and semantic segmentation.
Ranked #1 on Head Pose Estimation on BJUT-3D
In order to learn this general model family, this paper uses a method called Logistic Boosting Regression (LogitBoost) which can be seen as an additive weighted function regression from the statistical viewpoint.