Frontiers of Deep Learning: From Novel Application to Real-World Deployment
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Abstract (arXiv)
Deep learning continues to re-shape numerous fields, from natural language processing and imaging to data analytics and recommendation systems. This report studies two research papers that represent recent progress on deep learning from two largely different aspects: The first paper applied the transformer networks, which are typically used in language models, to improve the quality of synthetic aperture radar image by effectively reducing the speckle noise. The second paper presents an in-storage computing design solution to enable cost-efficient and high-performance implementations of deep learning recommendation systems. In addition to summarizing each paper in terms of motivation, key ideas and techniques, and evaluation results, this report also presents thoughts and discussions about possible future research directions. By carrying out in-depth study on these two representative papers and related references, this doctoral candidate has developed better understanding on the far-reaching impact and efficient implementation of deep learning models.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 22 of its 58 references that are in the map. First posted on arXiv 19 Jul 2024.
Builds on
22 of 58 references.
References · 22
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
- 2021Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutionscited by 1,962
- 2021SwinIR: Image Restoration Using Swin Transformercited by 1,887
- 2021Restormer: Efficient Transformer for High-Resolution Image Restorationcited by 1,627
- 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,558
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- 2017Neural Collaborative Filteringcited by 1,111
- 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 959
- 2021Uformer: A General U-Shaped Transformer for Image Restorationcited by 866
- 2016Wide & Deep Learning for Recommender Systemscited by 747
- 2019Deep Learning Recommendation Model for Personalization and Recommendation Systemscited by 327
10 more
- 2021Understanding and Overcoming the Challenges of Efficient Transformer Quantizationcited by 141
- 2022MatchFormer: Interleaving Attention in Transformers for Feature Matchingcited by 126
- 2022Towards Learning Universal Hyperparameter Optimizers with Transformerscited by 81
- 2020Deep Learning Meets SARcited by 60
- 2017SAR image despeckling through convolutional neural networkscited by 30
- 2022Towards Efficient Adversarial Training on Vision Transformerscited by 30
- 2023On Efficient Training of Large-Scale Deep Learning Models: A Literature Reviewcited by 26
- 2017SAR Image Despeckling Using a Convolutional Neural Networkcited by 24
- 2022Transformer-based SAR Image Despecklingcited by 15
- 2021A Competitive Edge: Can FPGAs Beat GPUs at DCNN Inference Acceleration in Resource-Limited Edge Computing Applications?cited by 6
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Citing papers per year: 2025: 1.
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