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Found 4 papers, 1 papers with code
A Scalable and Cloud-Native Hyperparameter Tuning System
In this paper, we introduce Katib: a scalable, cloud-native, and production-ready hyperparameter tuning system that is agnostic of the underlying machine learning framework.
Fine-Grained Age Estimation in the wild with Attention LSTM Networks
Then, we fine-tune the ResNets or the RoR on the target age datasets to extract the global features of face images.
Ranked #3 on
Age And Gender Classification
on Adience Age
(using extra training data)
Age Group and Gender Estimation in the Wild with Deep RoR Architecture
In this paper, we propose a new CNN based method for age group and gender estimation leveraging Residual Networks of Residual Networks (RoR), which exhibits better optimization ability for age group and gender classification than other CNN architectures. Moreover, two modest mechanisms based on observation of the characteristics of age group are presented to further improve the performance of age estimation. In order to further improve the performance and alleviate over-fitting problem, RoR model is pre-trained on ImageNet firstly, and then it is fune-tuned on the IMDB-WIKI-101 data set for further learning the features of face images, finally, it is used to fine-tune on Adience data set.
Ranked #5 on
Age And Gender Classification
on Adience Age
(using extra training data)
Pyramidal RoR for Image Classification
The Residual Networks of Residual Networks (RoR) exhibits excellent performance in the image classification task, but sharply increasing the number of feature map channels makes the characteristic information transmission incoherent, which losses a certain of information related to classification prediction, limiting the classification performance.
