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Found 5 papers, 2 papers with code
Train Once, Get a Family: State-Adaptive Balances for Offline-to-Online Reinforcement Learning
Existing solutions tackle this problem by imposing a policy constraint on the policy improvement objective in both offline and online learning.
Camera Bias in a Fine Grained Classification Task
We show that correlations between the camera used to acquire an image and the class label of that image can be exploited by convolutional neural networks (CNN), resulting in a model that "cheats" at an image classification task by recognizing which camera took the image and inferring the class label from the camera.
A study of the effect of the illumination model on the generation of synthetic training datasets
Our results show that the effect of the illumination model is important, comparable in significance to the network architecture.
Watermark retrieval from 3D printed objects via synthetic data training
We conclude that in our application domain of information retrieval from 3D printed objects, where access to the exact CAD files of the printed objects can be assumed, one can use inexpensive synthetic data to enhance neural network training, reducing the need for the labour intensive process of creating large amounts of hand labelled real data or the need to generate photorealistic synthetic data.
A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural Networks
Recent studies on multi-label image classification have focused on designing more complex architectures of deep neural networks such as the use of attention mechanisms and region proposal networks.
