CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning

Modern deep neural networks can easily overfit to biased training data containing corrupted labels or class imbalance. Sample re-weighting methods are popularly used to alleviate this data bias issue. Most current methods, however, require to manually pre-specify the weighting schemes as well as their additional hyper-parameters relying on the characteristics of the investigated problem and training data. This makes them fairly hard to be generally applied in practical scenarios, due to their significant complexities and inter-class variations of data bias situations. To address this issue, we propose a meta-model capable of adaptively learning an explicit weighting scheme directly from data. Specifically, by seeing each training class as a separate learning task, our method aims to extract an explicit weighting function with sample loss and task/class feature as input, and sample weight as output, expecting to impose adaptively varying weighting schemes to different sample classes based on their own intrinsic bias characteristics. Synthetic and real data experiments substantiate the capability of our method on achieving proper weighting schemes in various data bias cases, like the class imbalance, feature-independent and dependent label noise scenarios, and more complicated bias scenarios beyond conventional cases. Besides, the task-transferability of the learned weighting scheme is also substantiated, by readily deploying the weighting function learned on relatively smaller-scale CIFAR-10 dataset on much larger-scale full WebVision dataset. A performance gain can be readily achieved compared with previous SOAT ones without additional hyper-parameter tuning and meta gradient descent step. The general availability of our method for multiple robust deep learning issues, including partial-label learning, semi-supervised learning and selective classification, has also been validated.

Diagnosing Batch Normalization in Class Incremental Learning

Extensive researches have applied deep neural networks (DNNs) in class incremental learning (Class-IL). As building blocks of DNNs, batch normalization (BN) standardizes intermediate feature maps and has been widely validated to improve training stability and convergence. However, we claim that the direct use of standard BN in Class-IL models is harmful to both the representation learning and the classifier training, thus exacerbating catastrophic forgetting. In this paper we investigate the influence of BN on Class-IL models by illustrating such BN dilemma. We further propose BN Tricks to address the issue by training a better feature extractor while eliminating classification bias. Without inviting extra hyperparameters, we apply BN Tricks to three baseline rehearsal-based methods, ER, DER++ and iCaRL. Through comprehensive experiments conducted on benchmark datasets of Seq-CIFAR-10, Seq-CIFAR-100 and Seq-Tiny-ImageNet, we show that BN Tricks can bring significant performance gains to all adopted baselines, revealing its potential generality along this line of research.

Low-light Image Enhancement by Retinex Based Algorithm Unrolling and Adjustment

Motivated by their recent advances, deep learning techniques have been widely applied to low-light image enhancement (LIE) problem. Among which, Retinex theory based ones, mostly following a decomposition-adjustment pipeline, have taken an important place due to its physical interpretation and promising performance. However, current investigations on Retinex based deep learning are still not sufficient, ignoring many useful experiences from traditional methods. Besides, the adjustment step is either performed with simple image processing techniques, or by complicated networks, both of which are unsatisfactory in practice. To address these issues, we propose a new deep learning framework for the LIE problem. The proposed framework contains a decomposition network inspired by algorithm unrolling, and adjustment networks considering both global brightness and local brightness sensitivity. By virtue of algorithm unrolling, both implicit priors learned from data and explicit priors borrowed from traditional methods can be embedded in the network, facilitate to better decomposition. Meanwhile, the consideration of global and local brightness can guide designing simple yet effective network modules for adjustment. Besides, to avoid manually parameter tuning, we also propose a self-supervised fine-tuning strategy, which can always guarantee a promising performance. Experiments on a series of typical LIE datasets demonstrated the effectiveness of the proposed method, both quantitatively and visually, as compared with existing methods.

Exact Decomposition of Joint Low Rankness and Local Smoothness Plus Sparse Matrices

It is known that the decomposition in low-rank and sparse matrices (\textbf{L+S} for short) can be achieved by several Robust PCA techniques. Besides the low rankness, the local smoothness (\textbf{LSS}) is a vitally essential prior for many real-world matrix data such as hyperspectral images and surveillance videos, which makes such matrices have low-rankness and local smoothness properties at the same time. This poses an interesting question: Can we make a matrix decomposition in terms of \textbf{L\&LSS +S } form exactly? To address this issue, we propose in this paper a new RPCA model based on three-dimensional correlated total variation regularization (3DCTV-RPCA for short) by fully exploiting and encoding the prior expression underlying such joint low-rank and local smoothness matrices. Specifically, using a modification of Golfing scheme, we prove that under some mild assumptions, the proposed 3DCTV-RPCA model can decompose both components exactly, which should be the first theoretical guarantee among all such related methods combining low rankness and local smoothness. In addition, by utilizing Fast Fourier Transform (FFT), we propose an efficient ADMM algorithm with a solid convergence guarantee for solving the resulting optimization problem. Finally, a series of experiments on both simulations and real applications are carried out to demonstrate the general validity of the proposed 3DCTV-RPCA model.

An Efficient and Accurate Rough Set for Feature Selection, Classification and Knowledge Representation

This paper present a strong data mining method based on rough set, which can realize feature selection, classification and knowledge representation at the same time. Rough set has good interpretability, and is a popular method for feature selections. But low efficiency and low accuracy are its main drawbacks that limits its application ability. In this paper,corresponding to the accuracy, we first find the ineffectiveness of rough set because of overfitting, especially in processing noise attribute, and propose a robust measurement for an attribute, called relative importance.we proposed the concept of "rough concept tree" for knowledge representation and classification. Experimental results on public benchmark data sets show that the proposed framework achieves higher accurcy than seven popular or the state-of-the-art feature selection methods.

Revisiting Experience Replay: Continual Learning by Adaptively Tuning Task-wise Relationship

Continual learning requires models to learn new tasks while maintaining previously learned knowledge. Various algorithms have been proposed to address this real challenge. Till now, rehearsal-based methods, such as experience replay, have achieved state-of-the-art performance. These approaches save a small part of the data of the past tasks as a memory buffer to prevent models from forgetting previously learned knowledge. However, most of them treat every new task equally, i.e., fixed the hyperparameters of the framework while learning different new tasks. Such a setting lacks the consideration of the relationship/similarity between past and new tasks. For example, the previous knowledge/features learned from dogs are more beneficial for the identification of cats (new task), compared to those learned from buses. In this regard, we propose a meta learning algorithm based on bi-level optimization to adaptively tune the relationship between the knowledge extracted from the past and new tasks. Therefore, the model can find an appropriate direction of gradient during continual learning and avoid the serious overfitting problem on memory buffer. Extensive experiments are conducted on three publicly available datasets (i.e., CIFAR-10, CIFAR-100, and Tiny ImageNet). The experimental results demonstrate that the proposed method can consistently improve the performance of all baselines.

InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal Artifact Reduction in CT Images

During the computed tomography (CT) imaging process, metallic implants within patients always cause harmful artifacts, which adversely degrade the visual quality of reconstructed CT images and negatively affect the subsequent clinical diagnosis. For the metal artifact reduction (MAR) task, current deep learning based methods have achieved promising performance. However, most of them share two main common limitations: 1) the CT physical imaging geometry constraint is not comprehensively incorporated into deep network structures; 2) the entire framework has weak interpretability for the specific MAR task; hence, the role of every network module is difficult to be evaluated. To alleviate these issues, in the paper, we construct a novel interpretable dual domain network, termed InDuDoNet+, into which CT imaging process is finely embedded. Concretely, we derive a joint spatial and Radon domain reconstruction model and propose an optimization algorithm with only simple operators for solving it. By unfolding the iterative steps involved in the proposed algorithm into the corresponding network modules, we easily build the InDuDoNet+ with clear interpretability. Furthermore, we analyze the CT values among different tissues, and merge the prior observations into a prior network for our InDuDoNet+, which significantly improve its generalization performance. Comprehensive experiments on synthesized data and clinical data substantiate the superiority of the proposed methods as well as the superior generalization performance beyond the current state-of-the-art (SOTA) MAR methods. Code is available at \url{https://github.com/hongwang01/InDuDoNet_plus}.