Results 11 to 20 of about 298,825 (343)
JPEG steganography: A performance evaluation of quantization tables [PDF]
, 2009 The two most important aspects of any image based steganographic system are the imperceptibility and the capacity of the stego image. This paper evaluates the performance and efficiency of using optimized quantization tables instead of default JPEG ...
Al-Mohammad, A, Ghinea, G, Hierons, RM
core +7 more sources
How to Secure Valid Quantizations
Entropy, 2022 Canonical quantization has created many valid quantizations that require infinite-line coordinate variables. However, the half-harmonic oscillator, which is limited to the positive coordinate half, cannot receive a valid canonical quantization because of
John R. Klauder
doaj +1 more source
Autoregressive Image Generation using Residual Quantization [PDF]
Computer Vision and Pattern Recognition, 2022 For autoregressive (AR) modeling of high-resolution images, vector quantization (VQ) represents an image as a sequence of discrete codes. A short sequence length is important for an AR model to reduce its computational costs to consider long-range ...
Doyup Lee +4 more
semanticscholar +1 more source
A dichotomy color quantization algorithm for the HSI color space
Scientific Reports, 2023 Color quantization is used to obtain an image with the same number of pixels as the original but represented using fewer colors. Most existing color quantization algorithms are based on the Red Green Blue (RGB) color space, and there are few color ...
Xia Yu +5 more
doaj +1 more source
Gradient Estimation for Ultra Low Precision POT and Additive POT Quantization
IEEE Access, 2023 Deep learning networks achieve high accuracy for many classification tasks in computer vision and natural language processing. As these models are usually over-parameterized, the computations and memory required are unsuitable for power-constrained ...
Huruy Tesfai +4 more
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Learning Bilateral Clipping Parametric Activation for Low-Bit Neural Networks
Mathematics, 2023 Among various network compression methods, network quantization has developed rapidly due to its superior compression performance. However, trivial activation quantization schemes limit the compression performance of network quantization.
Yunlong Ding, Di-Rong Chen
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ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers [PDF]
Neural Information Processing Systems, 2022 How to efficiently serve ever-larger trained natural language models in practice has become exceptionally challenging even for powerful cloud servers due to their prohibitive memory/computation requirements.
Z. Yao +5 more
semanticscholar +1 more source
LLM-QAT: Data-Free Quantization Aware Training for Large Language Models [PDF]
Annual Meeting of the Association for Computational Linguistics, 2023 Several post-training quantization methods have been applied to large language models (LLMs), and have been shown to perform well down to 8-bits. We find that these methods break down at lower bit precision, and investigate quantization aware training ...
Ze-Chun Liu +8 more
semanticscholar +1 more source
Design Exploration of ReRAM-Based Crossbar for AI Inference
IEEE Access, 2021 ReRAM-based crossbar designs utilizing mixed-signal implementation has gained importance due to their low power, small size, low cost, and high throughput especially for multiply-and-add operations in AI-related applications.
Yasmin Halawani +2 more
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Proceedings of the National Academy of Sciences, 2003 We associate to the action of a compact Lie group G on a line bundle over a compact oriented even-dimensional manifold a virtual representation of G using a twisted version of the signature operator.
Guillemin, Victor +2 more
openaire +5 more sources