Papers Read on AI

Blind face restoration usually relies on facial priors, such as facial geometry prior or reference prior, to restore realistic and faithful details. However, very low-quality inputs cannot offer accurate geometric prior while high-quality references are inaccessible, limiting the applicability in real-world scenarios. In this work, we propose GFP-GAN that leverages rich and diverse priors encapsulated in a pretrained face GAN for blind face restoration. This Generative Facial Prior (GFP) is incorporated into the face restoration process via spatial feature transform layers, which allow our method to achieve a good balance of realness and fidelity.

2021: Xintao Wang, Yu Li, Honglun Zhang, Ying Shan

Ranked #1 on Blind Face Restoration on CelebA-Test

https://arxiv.org/pdf/2101.04061v2.pdf

Transformers have shown great potential in computer vision tasks. A common belief is their attention-based token mixer module contributes most to their competence. However, recent works show the attention-based module in transformers can be replaced by spatial MLPs and the resulted models still perform quite well. Based on this observation, we hypothesize that the general architecture of the transformers, instead of the specific token mixer module, is more essential to the model’s performance.

2021: Weihao Yu, Mi Luo, Pan Zhou, Chenyang Si, Yichen Zhou, Xinchao Wang, Jiashi Feng, Shuicheng Yan

https://arxiv.org/pdf/2111.11418v1.pdf

Deploying Machine Learning (ML) algorithms within databases is a challenge due to the varied computational footprints of modern ML algorithms and the myriad of database technologies each with their own restrictive syntax. We introduce an Apache Spark-based micro-service orchestration framework that extends database operations to include web service primitives. Our system can orchestrate web services across hundreds of machines and takes full advantage of cluster, thread, and asynchronous parallelism. Using this framework, we provide large scale clients for intelligent services such as speech, vision, search, anomaly detection, and text analysis. This allows users to integrate ready-to-use intelligence into any datastore with an Apache Spark connector.

2020: Mark Hamilton, Nick Gonsalves, Christina Lee, Anand Raman, Brendan Walsh, Siddhartha Prasad, Dalitso Banda, L. Zhang, Lei Zhang, W. Freeman

https://arxiv.org/pdf/2009.08044v2.pdf

Successful quantitative investment usually relies on precise predictions of the future movement of the stock price. Recently, machine learning based solutions have shown their capacity to give more accurate stock prediction and become indispensable components in modern quantitative investment systems. However, the i.i.d. assumption behind existing methods is inconsistent with the existence of diverse trading patterns in the stock market, which inevitably limits their ability to achieve better stock prediction performance. In this paper, we propose a novel architecture, Temporal Routing Adaptor (TRA)

2021: Hengxu Lin, D. Zhou, Weiqing Liu, J. Bian

https://arxiv.org/pdf/2106.12950v2.pdf

The scale, variety, and quantity of publicly available NLP datasets has grown rapidly as researchers propose new tasks, larger models, and novel benchmarks. Datasets is a community library for contemporary NLP designed to support this ecosystem.

 

2021: Quentin Lhoest, Albert Villanova del Moral, Yacine Jernite, A. Thakur, Patrick von Platen, Suraj Patil, Julien Chaumond, Mariama Drame, Julien Plu, Lewis Tunstall, Joe Davison, Mario vSavsko, Gunjan Chhablani, Bhavitvya Malik, Simon Brandeis, Teven Le Scao, Victor Sanh, Canwen Xu, Nicolas Patry, Angelina McMillan-Major, Philipp Schmid, Sylvain Gugger, Clement Delangue, Th'eo Matussiere, Lysandre Debut, Stas Bekman, Pierric Cistac, Thibault Goehringer, Victor Mustar, Franccois Lagunas, Alexander Rush, Thomas Wolf

https://arxiv.org/pdf/2109.02846v1.pdf

Vision transformer (ViT) has recently showed its strong capability in achieving comparable results to convolutional neural networks (CNNs) on image classification. However, vanilla ViT simply inherits the same architecture from the natural language processing directly, which is often not optimized for vision applications. Motivated by this, in this paper, we propose a new architecture that adopts the pyramid structure and employ a novel regional-to-local attention rather than global self-attention in vision transformers.

2021: Chun-Fu Chen, R. Panda, Quanfu Fan

https://arxiv.org/pdf/2106.02689v1.pdf

Remotely sensed geospatial data are critical for applications including precision agriculture, urban planning, disaster monitoring and response, and climate change research, among others.

2021: Adam J. Stewart, Caleb Robinson, Isaac A. Corley, Anthony Ortiz, Juan M. Lavista Ferres, Arindam Banerjee

https://arxiv.org/pdf/2111.08872v1.pdf

When engineers train deep learning models, they are very much “flying blind”. Commonly used approaches for realtime training diagnostics, such as monitoring the train/test loss, are limited. Assessing a network’s training process solely through these performance indicators is akin to debugging software without access to internal states through a debugger. To address this, we present COCKPIT, a collection of instruments that enable a closer look into the inner workings of a learning machine, and a more informative and meaningful status report for practitioners. It facilitates the identification of learning phases and failure modes, like ill chosen hyper parameters.

2021: Frank Schneider, Felix Dangel, Philipp Hennig

https://arxiv.org/pdf/2102.06604v2.pdf

When engineers train deep learning models, they are very much “flying blind”. Commonly used approaches for realtime training diagnostics, such as monitoring the train/test loss, are limited. Assessing a network’s training process solely through these performance indicators is akin to debugging software without access to internal states through a debugger. To address this, we present COCKPIT, a collection of instruments that enable a closer look into the inner workings of a learning machine, and a more informative and meaningful status report for practitioners. It facilitates the identification of learning phases and failure modes, like ill chosen hyper parameters.

2021: Frank Schneider, Felix Dangel, Philipp Hennig

https://arxiv.org/pdf/2102.06604v2.pdf

Mask classification is sufficiently general to solve both semantic and instance-level segmentation tasks in a unified manner using the exact same model, loss, and training procedure.

2021: Bowen Cheng1, Alexander G. Schwing, Alexander Kirillov

https://arxiv.org/abs/2107.06278v1