Universal adversarial patch attack for automatic checkout using perceptual and attentional bias
Adversarial examples are inputs with imperceptible perturbations that easily mislead deep
neural networks (DNNs). Recently, adversarial patch, with noise confined to a small and …
neural networks (DNNs). Recently, adversarial patch, with noise confined to a small and …
Hate speech detection via dual contrastive learning
The fast spread of hate speech on social media impacts the Internet environment and our
society by increasing prejudice and hurting people. Detecting hate speech has aroused …
society by increasing prejudice and hurting people. Detecting hate speech has aroused …
Contrastive learning for robust android malware familial classification
Due to its open-source nature, Android operating system has been the main target of
attackers to exploit. Malware creators always perform different code obfuscations on their …
attackers to exploit. Malware creators always perform different code obfuscations on their …
ADCL: Adversarial Distilled Contrastive Learning on lightweight models for self-supervised image classification
R Wu, H Liu, JB Li - Knowledge-Based Systems, 2023 - Elsevier
With the development of modern sensors, numerous images are collected in edge
application scenarios; however, their utilization is quite expensive because a massive effort …
application scenarios; however, their utilization is quite expensive because a massive effort …
Contrastive regularization for multimodal emotion recognition using audio and text
F Qian, J Han - arXiv preprint arXiv:2211.10885, 2022 - arxiv.org
Speech emotion recognition is a challenge and an important step towards more natural
human-computer interaction (HCI). The popular approach is multimodal emotion recognition …
human-computer interaction (HCI). The popular approach is multimodal emotion recognition …
CC2Vec: Combining Typed Tokens with Contrastive Learning for Effective Code Clone Detection
With the development of the open source community, the code is often copied, spread, and
evolved in multiple software systems, which brings uncertainty and risk to the software …
evolved in multiple software systems, which brings uncertainty and risk to the software …
[PDF][PDF] The Disadvantage of CNN versus DBN Image Classification Under Adversarial Conditions.
T Yang, DL Silver - Canadian AI, 2021 - assets.pubpub.org
Abstract We compare Convolutional Neural Networks (CNN) and Deep Belief Networks
(DBN) ability to withstand common image classification attacks. CNNs makes a strong …
(DBN) ability to withstand common image classification attacks. CNNs makes a strong …
Slot contrastive networks: A contrastive approach for representing objects
Unsupervised extraction of objects from low-level visual data is an important goal for further
progress in machine learning. Existing approaches for representing objects without labels …
progress in machine learning. Existing approaches for representing objects without labels …
Self-supervised spatial reasoning on multi-view line drawings
Spatial reasoning on multi-view line drawings by state-of-the-art supervised deep networks
is recently shown with puzzling low performances on the SPARE3D dataset. Based on the …
is recently shown with puzzling low performances on the SPARE3D dataset. Based on the …
[图书][B] Deep Neural Networks for Voice Control
LP Lugosch - 2023 - search.proquest.com
Voice control systems enable people to control their computers by speaking to them. After a
review of the state-of-the-art in sequence modeling, speech recognition, and language …
review of the state-of-the-art in sequence modeling, speech recognition, and language …