24 research outputs found
Easy Batch Normalization
It was shown that adversarial examples improve object recognition. But what
about their opposite side, easy examples? Easy examples are samples that the
machine learning model classifies correctly with high confidence. In our paper,
we are making the first step toward exploring the potential benefits of using
easy examples in the training procedure of neural networks. We propose to use
an auxiliary batch normalization for easy examples for the standard and robust
accuracy improvement
Towards Subject Agnostic Affective Emotion Recognition
This paper focuses on affective emotion recognition, aiming to perform in the
subject-agnostic paradigm based on EEG signals. However, EEG signals manifest
subject instability in subject-agnostic affective Brain-computer interfaces
(aBCIs), which led to the problem of distributional shift. Furthermore, this
problem is alleviated by approaches such as domain generalisation and domain
adaptation. Typically, methods based on domain adaptation confer comparatively
better results than the domain generalisation methods but demand more
computational resources given new subjects. We propose a novel framework,
meta-learning based augmented domain adaptation for subject-agnostic aBCIs. Our
domain adaptation approach is augmented through meta-learning, which consists
of a recurrent neural network, a classifier, and a distributional shift
controller based on a sum-decomposable function. Also, we present that a neural
network explicating a sum-decomposable function can effectively estimate the
divergence between varied domains. The network setting for augmented domain
adaptation follows meta-learning and adversarial learning, where the controller
promptly adapts to new domains employing the target data via a few
self-adaptation steps in the test phase. Our proposed approach is shown to be
effective in experiments on a public aBICs dataset and achieves similar
performance to state-of-the-art domain adaptation methods while avoiding the
use of additional computational resources.Comment: To Appear in MUWS workshop at the 32nd ACM International Conference
on Information and Knowledge Management (CIKM) 202
RESEARCH ON IIOT SECURITY: NOVEL MACHINE LEARNING-BASED INTRUSION DETECTION USING TCP/IP PACKETS
The Industrial Internet of Things (IIoT) explosive expansion has raised questions regarding the safety of industrial systems. Networks like these are crucially protected from a variety of cyber threats by intrusion detection systems (IDSs). In order to detect intrusions in the IIoT environment utilizing TCP/IP packets, this work introduces a novel Hybrid Deep Convolutional Autoencoder and Splinted Decision Tree (HDCA-SDT) technique. High-level features are extracted from the unprocessed TCP/IP packet data using the DCA. The retrieved features are then classified using the SDT algorithm into various intrusion categories. In order to enable quicker decision-making yet preserve accurate results, the SDT technique effectively divides the feature space. The NSL-KDD dataset is used to train and assess the model. The efficiency of the suggested hybrid strategy is shown by experimental findings. Comparing the proposed hybrid approach to conventional intrusion detection methods, it acquired higher detection accuracy. The model also demonstrates robustness to fluctuations in traffic on the network and possesses the ability to identify known and unidentified intrusions with high recall rates
Spiking-Diffusion: Vector Quantized Discrete Diffusion Model with Spiking Neural Networks
Spiking neural networks (SNNs) have tremendous potential for energy-efficient
neuromorphic chips due to their binary and event-driven architecture. SNNs have
been primarily used in classification tasks, but limited exploration on image
generation tasks. To fill the gap, we propose a Spiking-Diffusion model, which
is based on the vector quantized discrete diffusion model. First, we develop a
vector quantized variational autoencoder with SNNs (VQ-SVAE) to learn a
discrete latent space for images. With VQ-SVAE, image features are encoded
using both the spike firing rate and postsynaptic potential, and an adaptive
spike generator is designed to restore embedding features in the form of spike
trains. Next, we perform absorbing state diffusion in the discrete latent space
and construct a diffusion image decoder with SNNs to denoise the image. Our
work is the first to build the diffusion model entirely from SNN layers.
Experimental results on MNIST, FMNIST, KMNIST, and Letters demonstrate that
Spiking-Diffusion outperforms the existing SNN-based generation model. We
achieve FIDs of 37.50, 91.98, 59.23 and 67.41 on the above datasets
respectively, with reductions of 58.60\%, 18.75\%, 64.51\%, and 29.75\% in FIDs
compared with the state-of-art work.Comment: Under Revie
Benchmarking Robustness of Adaptation Methods on Pre-trained Vision-Language Models
Various adaptation methods, such as LoRA, prompts, and adapters, have been
proposed to enhance the performance of pre-trained vision-language models in
specific domains. The robustness of these adaptation methods against
distribution shifts have not been studied. In this study, we assess the
robustness of 11 widely-used adaptation methods across 4 vision-language
datasets under multimodal corruptions. Concretely, we introduce 7 benchmark
datasets, including 96 visual and 87 textual corruptions, to investigate the
robustness of different adaptation methods, the impact of available adaptation
examples, and the influence of trainable parameter size during adaptation. Our
analysis reveals that: 1) Adaptation methods are more sensitive to text
corruptions than visual corruptions. 2) Full fine-tuning does not consistently
provide the highest robustness; instead, adapters can achieve better robustness
with comparable clean performance. 3) Contrary to expectations, our findings
indicate that increasing the number of adaptation data and parameters does not
guarantee enhanced robustness; instead it results in even lower robustness. We
hope this study could benefit future research in the development of robust
multimodal adaptation methods. The benchmark, code, and dataset used in this
study can be accessed at \url{https://adarobustness.github.io}
Domain Generalization in Machine Learning Models for Wireless Communications: Concepts, State-of-the-Art, and Open Issues
Data-driven machine learning (ML) is promoted as one potential technology to
be used in next-generations wireless systems. This led to a large body of
research work that applies ML techniques to solve problems in different layers
of the wireless transmission link. However, most of these applications rely on
supervised learning which assumes that the source (training) and target (test)
data are independent and identically distributed (i.i.d). This assumption is
often violated in the real world due to domain or distribution shifts between
the source and the target data. Thus, it is important to ensure that these
algorithms generalize to out-of-distribution (OOD) data. In this context,
domain generalization (DG) tackles the OOD-related issues by learning models on
different and distinct source domains/datasets with generalization capabilities
to unseen new domains without additional finetuning. Motivated by the
importance of DG requirements for wireless applications, we present a
comprehensive overview of the recent developments in DG and the different
sources of domain shift. We also summarize the existing DG methods and review
their applications in selected wireless communication problems, and conclude
with insights and open questions
A Recipe for Well-behaved Graph Neural Approximations of Complex Dynamics
Data-driven approximations of ordinary differential equations offer a
promising alternative to classical methods in discovering a dynamical system
model, particularly in complex systems lacking explicit first principles. This
paper focuses on a complex system whose dynamics is described with a system of
ordinary differential equations, coupled via a network adjacency matrix.
Numerous real-world systems, including financial, social, and neural systems,
belong to this class of dynamical models. We propose essential elements for
approximating such dynamical systems using neural networks, including necessary
biases and an appropriate neural architecture. Emphasizing the differences from
static supervised learning, we advocate for evaluating generalization beyond
classical assumptions of statistical learning theory. To estimate confidence in
prediction during inference time, we introduce a dedicated null model. By
studying various complex network dynamics, we demonstrate the neural network's
ability to approximate various dynamics, generalize across complex network
structures, sizes, and statistical properties of inputs. Our comprehensive
framework enables deep learning approximations of high-dimensional,
non-linearly coupled complex dynamical systems
Benchmarking Robustness of Text-Image Composed Retrieval
Text-image composed retrieval aims to retrieve the target image through the
composed query, which is specified in the form of an image plus some text that
describes desired modifications to the input image. It has recently attracted
attention due to its ability to leverage both information-rich images and
concise language to precisely express the requirements for target images.
However, the robustness of these approaches against real-world corruptions or
further text understanding has never been studied. In this paper, we perform
the first robustness study and establish three new diversified benchmarks for
systematic analysis of text-image composed retrieval against natural
corruptions in both vision and text and further probe textural understanding.
For natural corruption analysis, we introduce two new large-scale benchmark
datasets, CIRR-C and FashionIQ-C for testing in open domain and fashion domain
respectively, both of which apply 15 visual corruptions and 7 textural
corruptions. For textural understanding analysis, we introduce a new diagnostic
dataset CIRR-D by expanding the original raw data with synthetic data, which
contains modified text to better probe textual understanding ability including
numerical variation, attribute variation, object removal, background variation,
and fine-grained evaluation. The code and benchmark datasets are available at
https://github.com/SunTongtongtong/Benchmark-Robustness-Text-Image-Compose-Retrieval.Comment: Accepted by R0-FoMo: Workshop on Robustness of Few-shot and Zero-shot
Learning in Foundation Models at NeurIPS 202
Spectral-DP: Differentially Private Deep Learning through Spectral Perturbation and Filtering
Differential privacy is a widely accepted measure of privacy in the context
of deep learning algorithms, and achieving it relies on a noisy training
approach known as differentially private stochastic gradient descent (DP-SGD).
DP-SGD requires direct noise addition to every gradient in a dense neural
network, the privacy is achieved at a significant utility cost. In this work,
we present Spectral-DP, a new differentially private learning approach which
combines gradient perturbation in the spectral domain with spectral filtering
to achieve a desired privacy guarantee with a lower noise scale and thus better
utility. We develop differentially private deep learning methods based on
Spectral-DP for architectures that contain both convolution and fully connected
layers. In particular, for fully connected layers, we combine a block-circulant
based spatial restructuring with Spectral-DP to achieve better utility. Through
comprehensive experiments, we study and provide guidelines to implement
Spectral-DP deep learning on benchmark datasets. In comparison with
state-of-the-art DP-SGD based approaches, Spectral-DP is shown to have
uniformly better utility performance in both training from scratch and transfer
learning settings.Comment: Accepted in 2023 IEEE Symposium on Security and Privacy (SP