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Found 3 papers, 2 papers with code
Active Learning of Ordinal Embeddings: A User Study on Football Data
We adapt an entropy-based active learning method with recent work from triplet mining to collect easy-to-answer but still informative annotations from human participants and use them to train a deep convolutional network that generalizes to unseen samples.
Active Ordinal Querying for Tuplewise Similarity Learning
Many machine learning tasks such as clustering, classification, and dataset search benefit from embedding data points in a space where distances reflect notions of relative similarity as perceived by humans.
Data-Free Knowledge Distillation for Deep Neural Networks
Recent advances in model compression have provided procedures for compressing large neural networks to a fraction of their original size while retaining most if not all of their accuracy.
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