Correction: Shang, R.; Liu, R.; Xu, M.; Liu, Y.; Dash, J.; Ge, Q. Determining the Start of the Growing Season from MODIS Data in the Indian Monsoon Region: Identifying Available Data in the Rainy Season and Modeling the Varied Vegetation Growth Trajectories, Remote Sens. 2018, 10, 122

Shang, Rong; Xu, Mingzhu; Liu, Yang; Dash, Jadunandan; Ge, Quansheng

doi:10.3390/rs11080939

Cited by 2 publications

(2 citation statements)

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“…Therefore, data-free quantization has been proposed for compression under privacy protection. Among some excellent works (Nagel et al 2019;Banner et al 2018;Cai et al 2020;Nagel et al 2019), generative methods (Choi et al 2021;Xu et al 2020;Choi et al 2022;Qian et al 2023b,a;Li et al 2023;Shang et al 2023;Choi et al 2020) have drawn much attention due to their great performance. These methods synthesize fake data through generators and use them to calibrate or fine-tune the quantified model.…”

Section: Introductionmentioning

confidence: 99%

Robustness-Guided Image Synthesis for Data-Free Quantization

Bai,

Yang,

Chu

et al. 2024

AAAI

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Quantization has emerged as a promising direction for model compression. Recently, data-free quantization has been widely studied as a promising method to avoid privacy concerns, which synthesizes images as an alternative to real training data. Existing methods use classification loss to ensure the reliability of the synthesized images. Unfortunately, even if these images are well-classified by the pre-trained model, they still suffer from low semantics and homogenization issues. Intuitively, these low-semantic images are sensitive to perturbations, and the pre-trained model tends to have inconsistent output when the generator synthesizes an image with low semantics. To this end, we propose Robustness-Guided Image Synthesis (RIS), a simple but effective method to enrich the semantics of synthetic images and improve image diversity, further boosting the performance of data-free compression tasks. Concretely, we first introduce perturbations on input and model weight, then define the inconsistency metrics at feature and prediction levels before and after perturbations. On the basis of inconsistency on two levels, we design a robustness optimization objective to eliminate low-semantic images. Moreover, we also make our approach diversity-aware by forcing the generator to synthesize images with small correlations. With RIS, we achieve state-of-the-art performance for various settings on data-free quantization and can be extended to other data-free compression tasks.

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Section: Introductionmentioning

confidence: 99%

Robustness-Guided Image Synthesis for Data-Free Quantization

Bai,

Yang,

Chu

et al. 2024

AAAI

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“…Previous studies have focused on examining crop phenology over large area using satellite data with high temporal resolution in order to overcome cloud coverage issues [20,28]. Shang et al [29] applied refined cloud detection approach using MODIS data to separate cloudy scenes from the clear ones to model vegetation growth across India. Similarly, Chakraborty et al [30] measured the crop phenology trend of the monsoon cropland vegetation and its relationship with rainfall using the Global Inventory Modelling and Mapping Studies (GIMMS) datasets for all of India.…”

Section: Introductionmentioning

confidence: 99%

Synergistic Use of Radar and Optical Satellite Data for Improved Monsoon Cropland Mapping in India

Qadir

Mondal

2020

Remote Sensing

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Monsoon crops play a critical role in Indian agriculture, hence, monitoring these crops is vital for supporting economic growth and food security for the country. However, monitoring these crops is challenging due to limited availability of optical satellite data due to cloud cover during crop growth stages, landscape heterogeneity, and small field sizes. In this paper, our objective is to develop a robust methodology for high-resolution (10 m) monsoon cropland mapping appropriate for different agro-ecological regions (AER) in India. We adapted a synergistic approach of combining Sentinel-1 Synthetic Aperture Radar (SAR) data with Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 optical data using the Google Earth Engine platform. We developed a new technique, Radar Optical cross Masking (ROM), for separating cropland from non-cropland by masking out forest, plantation, and other non-dynamic features. The methodology was tested for five different AERs in India, representing a wide diversity in agriculture, soil, and climatic variations. Our findings indicate that the overall accuracy obtained by using the SAR-only approach is 90%, whereas that of the combined approach is 93%. Our proposed methodology is particularly effective in regions with cropland mixed with tree plantation/mixed forest, typical of smallholder dominated tropical countries. The proposed agriculture mask, ROM, has high potential to support the global agriculture monitoring missions of Geo Global Agriculture Monitoring (GEOGLAM) and Sentinel-2 for Agriculture (S2Agri) project for constructing a dynamic monsoon cropland mask.

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Correction: Shang, R.; Liu, R.; Xu, M.; Liu, Y.; Dash, J.; Ge, Q. Determining the Start of the Growing Season from MODIS Data in the Indian Monsoon Region: Identifying Available Data in the Rainy Season and Modeling the Varied Vegetation Growth Trajectories, Remote Sens. 2018, 10, 122

Abstract: The authors wish to make the following corrections to this paper [...]

Cited by 2 publications

References 1 publication

Robustness-Guided Image Synthesis for Data-Free Quantization

Robustness-Guided Image Synthesis for Data-Free Quantization

Synergistic Use of Radar and Optical Satellite Data for Improved Monsoon Cropland Mapping in India

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