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Found 3 papers, 2 papers with code
Few-Shot Batch Incremental Road Object Detection via Detector Fusion
Incremental few-shot learning has emerged as a new and challenging area in deep learning, whose objective is to train deep learning models using very few samples of new class data, and none of the old class data.
Speech Denoising Without Clean Training Data: A Noise2Noise Approach
This paper tackles the problem of the heavy dependence of clean speech data required by deep learning based audio-denoising methods by showing that it is possible to train deep speech denoising networks using only noisy speech samples.
Estimation and Applications of Quantiles in Deep Binary Classification
We quantify the uncertainty of the class probabilities in terms of prediction intervals, and develop individualized confidence scores that can be used to decide whether a prediction is reliable or not at scoring time.
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