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Found 5 papers, 4 papers with code
Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision
We theoretically derive an optimal acquisition function for AL in this setting.
DoorGym: A Scalable Door Opening Environment And Baseline Agent
We introduce DoorGym, an open-source door opening simulation framework designed to utilize domain randomization to train a stable policy.
Explanation-Based Attention for Semi-Supervised Deep Active Learning
We introduce an attention mechanism to improve feature extraction for deep active learning (AL) in the semi-supervised setting.
Explain to Fix: A Framework to Interpret and Correct DNN Object Detector Predictions
We qualitatively and quantitatively show that the proposed explanation method can be used to find image features which cause failures in DNN object detection.
DNN Feature Map Compression using Learned Representation over GF(2)
In this paper, we introduce a method to compress intermediate feature maps of deep neural networks (DNNs) to decrease memory storage and bandwidth requirements during inference.
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