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"LibFewShot: A Comprehensive Library for Few-Shot Learning" by Wenbin Li, Ziyi Wang et al.

Few-shot learning, especially few-shot image classification, has received increasing attention and witnessed significant advances in recent years. Some recent studies implicitly show that many generic techniques or “tricks”, such as data augmentation, pre-training, knowledge distillation, and self-supervision, may greatly boost the performance of a few-shot learning method. Moreover, different works may employ different software platforms, backbone architectures and input image sizes, making fair comparisons difficult and practitioners struggle with reproducibility. To address these situations, we propose a comprehensive library for few-shot learning (LibFewShot) by re-implementing eighteen state-of-the-art few-shot learning methods in a unified framework with the same single codebase in PyTorch. Furthermore, based on LibFewShot, we provide comprehensive evaluations on multiple benchmarks with various backbone architectures to evaluate common pitfalls and effects of different train ....

Benchmark Testing , Deep Learning , Hair Comparison , Few Shot Learning , Image Classification , Task Analysis , Unified Framework ,

GitHub - continuousml/Awesome-Out-Of-Distribution-Detection: A professionally curated list of papers, tutorials, books, videos, articles and open-source libraries etc for Out-of-distribution detection, robustness, and generalization

A professionally curated list of papers, tutorials, books, videos, articles and open-source libraries etc for Out-of-distribution detection, robustness, and generalization - GitHub - continuousml/Awesome-Out-Of-Distribution-Detection: A professionally curated list of papers, tutorials, books, videos, articles and open-source libraries etc for Out-of-distribution detection, robustness, and generalization ....

Sri Lanka , Dustin Tran , Thomasg Dietterich , Alexander Meinke , Yibo Zhou , Jie Ren , Vaishnavh Nagarajan , Balaji Lakshminarayanan , Neeraj Varshney , Sharon Yixuan Li , Jasper Snoek , Examples In Neural Networks , Presence Of Limited , Mitigating Neural Network Overconfidence , Confidence For Detection In Neural Networks , Energy Regularization Loss For Detection , Reliability Of Image Detection In Neural Networks , Detection In Deep Neural Networks , Detection Using Union , Networks Can Improve Robustness , Hopfield Energy , A Unified Survey On , Detection For Graph Neural Networks , Energy Based Model , Neural Networks , Why Relu Networks Yield High ,

AI Research Blog - The Transformer Blueprint: A Holistic Guide to the Transformer Neural Network Architecture

A deep dive into Transformer a neural network architecture that was introduced in the famous paper “attention is all you need” in 2017, its applications, impacts, challenges and future directions ....

United States , Dominican Republic , New South Wales , Basil Mustafa , Hesslow Daniel , Dani Yogatama , Vinhq Tran , Tao Qin , Saining Xie , Mishra Gaurav , Huishuai Zhang , Shuai Bai , Sergio Gomez Colmenarejo , Aidann Gomez , Kristina Toutanova , Alaaeldin El Nouby , Michael Laskin , Jacob Andreas , Ivo Danihelka , Soravit Changpinyo , Jacob Devlin , Donald Metzler , Gabriel Barth Maron , Gideon Mann , Colin Raffel , Elvis Saravia ,

Bertrand Meyer's technology+ blog » Blog Archive New article: scenarios versus OO requirements

Bertrand Meyer's technology+ blog » Blog Archive New article: scenarios versus OO requirements
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Alexandr Naumchev , Sophie Ebersold , Maria Naumcheva , Jean Michel Bruel , Bertrand Meyer , Florian Galinier , Journal Of Object Technology , Object Oriented Requirements , Unified Framework , Object Technology , Business Analysis ,

Solving the security challenges of public cloud

Solving the security challenges of public cloud
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Public Cloud , Security Events , Security Posture , Standardized Approach , Enterprise Organizations , Security Threats , Security Data , Cloud Security , Unified Framework , பொது மேகம் , உரத்த பாதுகாப்பு ,