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pro vyhledávání: '"affine transformation"'
Autor:
Diao, Wenhui, Yu, Haichen, Kang, Kaiyue, Ling, Tong, Liu, Di, Feng, Yingchao, Bi, Hanbo, Ren, Libo, Li, Xuexue, Mao, Yongqiang, Sun, Xian
Aerial Remote Sensing (ARS) vision tasks pose significant challenges due to the unique characteristics of their viewing angles. Existing research has primarily focused on algorithms for specific tasks, which have limited applicability in a broad rang
Externí odkaz:
http://arxiv.org/abs/2409.13366
Autor:
Kang, Jiale
Low-Rank Adaptation (LoRA) has achieved remarkable training results by freezing the original weights and training only low-rank matrices, establishing itself as the predominant fine-tuning method for LLMs. In pursuit of performance closer to full-par
Externí odkaz:
http://arxiv.org/abs/2409.15371
DAFT-GAN: Dual Affine Transformation Generative Adversarial Network for Text-Guided Image Inpainting
In recent years, there has been a significant focus on research related to text-guided image inpainting. However, the task remains challenging due to several constraints, such as ensuring alignment between the image and the text, and maintaining cons
Externí odkaz:
http://arxiv.org/abs/2408.04962
Autor:
Ueda, Jun, Kwon, Hyukbin
With the increasing integration of cyber-physical systems (CPS) into critical applications, ensuring their resilience against cyberattacks is paramount. A particularly concerning threat is the vulnerability of CPS to deceptive attacks that degrade sy
Externí odkaz:
http://arxiv.org/abs/2408.10177
Autor:
Ueda, Jun, Blevins, Jacob
This paper demonstrates the viability of perfectly undetectable affine transformation attacks against robotic manipulators where intelligent attackers can inject multiplicative and additive false data while remaining completely hidden from system use
Externí odkaz:
http://arxiv.org/abs/2405.11047
Autor:
Ma, Yuexiao, Li, Huixia, Zheng, Xiawu, Ling, Feng, Xiao, Xuefeng, Wang, Rui, Wen, Shilei, Chao, Fei, Ji, Rongrong
The significant resource requirements associated with Large-scale Language Models (LLMs) have generated considerable interest in the development of techniques aimed at compressing and accelerating neural networks. Among these techniques, Post-Trainin
Externí odkaz:
http://arxiv.org/abs/2403.12544
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Autor:
Torpey, David, Klein, Richard
The standard approach to modern self-supervised learning is to generate random views through data augmentations and minimise a loss computed from the representations of these views. This inherently encourages invariance to the transformations that co
Externí odkaz:
http://arxiv.org/abs/2402.09071
Akademický článek
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