Performance Analysis of Different Models for Twitter Sentiment

Joshi, Deepali; Kankurti, Tasmiya Tamreen N.; Padalkar, Akshada; Deshmukh, Rutvik; Kadam, Shailesh N.; Vartak, Tanay

doi:10.1007/978-981-33-4859-2_11

Search citation statements

Order By: Relevance

Paper Sections

Select...

Compressed Sensing Using Generative Models1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

Publication Types

Select...

Other1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although the non-convexity of the optimization problem, CSGMs outperform conventional sparsity-based compressed sensing solvers on relatively simple source distributions such as human face images (Liu et al, 2015). Theoretical analysis and improvement have been reported by Joshi et al (2021) whereas the techniques of inverting the generative models have been investigated in (Creswell and Bharath, 2018;Lei et al, 2019;Asim et al, 2020;Jalal et al, 2021).…”

Section: Compressed Sensing Using Generative Modelsmentioning

confidence: 99%

Deep Joint Source-Channel Coding with Iterative Source Error Correction

Lee¹,

Hu²,

Kim³

2023

Preprint

View full text Add to dashboard Cite

In this paper, we propose an iterative source error correction (ISEC) decoding scheme for deep-learning-based joint source-channel coding (Deep JSCC). Given a noisy codeword received through the channel, we use a Deep JSCC encoder and decoder pair to update the codeword iteratively to find a (modified) maximum a-posteriori (MAP) solution. For efficient MAP decoding, we utilize a neural network-based denoiser to approximate the gradient of the logprior density of the codeword space. Albeit the non-convexity of the optimization problem, our proposed scheme improves various distortion and perceptual quality metrics from the conventional one-shot (non-iterative) Deep JSCC decoding baseline. Furthermore, the proposed scheme produces more reliable source reconstruction results compared to the baseline when the channel noise characteristics do not match the ones used during training.

show abstract

Section: Compressed Sensing Using Generative Modelsmentioning

confidence: 99%