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- AIM 2019 Challenge on Constrained Super-Resolution: Methods and Results63
This paper reviews the AIM 2019 challenge on constrained example-based single image super-resolution with focus on proposed solutions and results and gauges the state-of-the-art in single imagesuper-resolution.
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- Deep Image Compression with Residual Learning29
An end-to-end image compression framework based on deep residual learning is proposed, which outperforms JPEG and JPEG2000 by PSNR and MS-SSIM at low BPP (bit per pixel).
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- Domain Adaptation for Intelligent Fault Diagnosis under Different Working Conditions4
A conditional maximum mean discrepancy (CMMD) based domain adaptation method is introduced to improve classification accuracy and generalization ability for cross-domain bearing fault diagnosis and the unsupervised learning method with unlabeled target data can promote the practical application of the proposed algorithm.
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- Deep compression for real-time high-precision SAR image ship detection1
A new model compression scheme to learn a slim ship detector named Lite-YOLOv4, modifying network structure to be lightweight and improving polarization-based channel pruning to generate a compact model is proposed.
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- An Once-for-All Budgeted Pruning Framework for ConvNets Considering Input Resolution1
An efficient once-for-all budgeted pruning framework (OFARPruning) to find many compact network architectures close to winner tickets in the early training stage considering the effect of input resolution during the pruning process, and achieves competitive performance as the conventional pruning methods with much higher efficiency.
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- Adaptive Structured Sparse Network for Efficient CNNs with Feature Regularization.–
The proposed Feature Regularization method can improve sparsity level in intermediate features by 60% to over 95% through pruning along the channel dimension for each pixel, thus relieving the computational and memory burden.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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