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Cesar F. Caiafa

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4 papers

2 author rows

ICML Conference 2024 Conference Paper

Diffusion Models Demand Contrastive Guidance for Adversarial Purification to Advance

Mingyuan Bai
Wei Huang
Tenghui Li
Andong Wang
Junbin Gao
Cesar F. Caiafa
Qibin Zhao

In adversarial defense, adversarial purification can be viewed as a special generation task with the purpose to remove adversarial attacks and diffusion models excel in adversarial purification for their strong generative power. With different predetermined generation requirements, various types of guidance have been proposed, but few of them focuses on adversarial purification. In this work, we propose to guide diffusion models for adversarial purification using contrastive guidance. We theoretically derive the proper noise level added in the forward process diffusion models for adversarial purification from a feature learning perspective. For the reverse process, it is implied that the role of contrastive loss guidance is to facilitate the evolution towards the signal direction. From the theoretical findings and implications, we design the forward process with the proper amount of Gaussian noise added and the reverse process with the gradient of contrastive loss as the guidance of diffusion models for adversarial purification. Empirically, extensive experiments on CIFAR-10, CIFAR-100, the German Traffic Sign Recognition Benchmark and ImageNet datasets with ResNet and WideResNet classifiers show that our method outperforms most of current adversarial training and adversarial purification methods by a large improvement.

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EAAI Journal 2024 Journal Article

Enabling temporal–spectral decoding in multi-class single-side upper limb classification

Hao Jia
Shuning Han
Cesar F. Caiafa
Feng Duan
Yu Zhang
Zhe Sun
Jordi Solé-Casals

This manuscript presents a novel approach for decoding pre-movement patterns from brain signals using a two-stage-training temporal–spectral neural network (TTSNet). The TTSNet employs a combination of filter bank task-related component analysis (FBTRCA) and convolutional neural network (CNN) techniques to enhance the classification of single-upper limb movements in non-invasive brain–computer interfaces (BCIs). In our previous work, we introduced the FBTRCA method which utilized filter banks and spatial filters to handle spectral and spatial information, respectively. However, we observed limitations in the temporal decoding phase, where correlation features failed to effectively utilize temporal information because of misaligned onset and noisy spikes. To address this issue, our proposed method focuses on analyzing multi-channel signals in the temporal–spectral domain. The TTSNet first divides the signals into various filter banks, employing task-related component analysis to reduce dimensionality and eliminate noise, respectively. Subsequently, a CNN is employed to optimize the temporal characteristics of the signals and extract class-related features. Finally, the class-related features from all filter banks are concatenated and classified using the fully connected layer. To evaluate the effectiveness of our proposed method, we conducted experiments on two publicly available datasets. In binary classification tasks, the TTSNet achieved an improved accuracy of 0. 7707 ± 0. 1168, surpassing the performance of EEGNet (accuracy: 0. 7340 ± 0. 1246) and FBTRCA (accuracy: 0. 7487 ± 0. 1250). In multi-class tasks, TTSNet achieved an accuracy of 0. 4588 ± 0. 0724, exhibiting a 4. 27% and 3. 95% accuracy increase over EEGNet and FBTRCA, respectively. The findings of this study suggest that the proposed TTSNet method holds promise for detecting limb movements and assisting in the rehabilitation of stroke patients. The classification of single-side limb movements is expected to facilitate the interaction between patients and external environment by increasing the number of control commands in BCIs.

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ICML Conference 2023 Conference Paper

Alternating Local Enumeration (TnALE): Solving Tensor Network Structure Search with Fewer Evaluations

Chao Li 0013
Junhua Zeng
Chunmei Li
Cesar F. Caiafa
Qibin Zhao

Tensor network (TN) is a powerful framework in machine learning, but selecting a good TN model, known as TN structure search (TN-SS), is a challenging and computationally intensive task. The recent approach TNLS (Li et al. , 2022) showed promising results for this task. However, its computational efficiency is still unaffordable, requiring too many evaluations of the objective function. We propose TnALE, a surprisingly simple algorithm that updates each structure-related variable alternately by local enumeration, greatly reducing the number of evaluations compared to TNLS. We theoretically investigate the descent steps for TNLS and TnALE, proving that both the algorithms can achieve linear convergence up to a constant if a sufficient reduction of the objective is reached in each neighborhood. We further compare the evaluation efficiency of TNLS and TnALE, revealing that $\Omega(2^K)$ evaluations are typically required in TNLS for reaching the objective reduction, while ideally $O(KR)$ evaluations are sufficient in TnALE, where $K$ denotes the dimension of search space and $R$ reflects the “low-rankness” of the neighborhood. Experimental results verify that TnALE can find practically good TN structures with vastly fewer evaluations than the state-of-the-art algorithms.

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JBHI Journal 2023 Journal Article

Multi-Class Classification of Upper Limb Movements With Filter Bank Task-Related Component Analysis

Hao Jia
Fan Feng
Cesar F. Caiafa
Feng Duan
Yu Zhang
Zhe Sun
Jordi Solé-Casals

The classification of limb movements can provide with control commands in non-invasive brain-computer interface. Previous studies on the classification of limb movements have focused on the classification of left/right limbs; however, the classification of different types of upper limb movements has often been ignored despite that it provides more active-evoked control commands in the brain-computer interface. Nevertheless, few machine learning method can be used as the state-of-the-art method in the multi-class classification of limb movements. This work focuses on the multi-class classification of upper limb movements and proposes the multi-class filter bank task-related component analysis (mFBTRCA) method, which consists of three steps: spatial filtering, similarity measuring and filter bank selection. The spatial filter, namely the task-related component analysis, is first used to remove noise from EEG signals. The canonical correlation measures the similarity of the spatial-filtered signals and is used for feature extraction. The correlation features are extracted from multiple low-frequency filter banks. The minimum-redundancy maximum-relevance selects the essential features from all the correlation features, and finally, the support vector machine is used to classify the selected features. The proposed method compared against previously used models is evaluated using two datasets. mFBTRCA achieved a classification accuracy of 0. 4193 $\pm$ 0. 0780 (7 classes) and 0. 4032 $\pm$ 0. 0714 (5 classes), respectively, which improves on the best accuracies achieved using the compared methods (0. 3590 $\pm$ 0. 0645 and 0. 3159 $\pm$ 0. 0736, respectively). The proposed method is expected to provide more control commands in the applications of non-invasive brain-computer interfaces.

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