This paper removes near-duplicates from Fashion-MNIST to improve testing accuracy.
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5 results for “near-duplicates”
problem Near-duplicate images in Fashion-MNIST increase testing accuracy, reducing dataset quality.
method Identified and removed near-duplicate images between training and testing sets.
result Improved dataset quality for better testing accuracy in machine learning models.
This study benchmarks deep learning for unsupervised near-duplicate image detection.
problem Detecting near-duplicates in large image datasets with high specificity.
method Binary classification using Receiver Operating Curve (ROC) for comparison of different descriptors.
result Fine-tuning deep convolutional networks generally outperforms off-the-shelf features, with best performance on MFND dataset.
ProbMinHash improves Jaccard similarity hashing for big data applications.
problem Efficiently estimating set similarities in big data with weighted elements.
method Locality-sensitive hash algorithms that calculate signatures collectively.
result Significantly faster than the original approach, with improved estimation error.
Robustness Analysis of Hottopixx, a Linear Programming Model for Factoring Nonnegative Matricesstat.ML
Although nonnegative matrix factorization (NMF) is NP-hard in general, it has been shown very recently that it is tractable under the assumption that the input nonnegative data matrix is close to being separable (separability requires that all columns of the input matrix belongs to the cone spanned by a small subset of…
New LSH methods for tensor data improve efficiency and space usage.
problem Efficiency and space usage issues in LSH for tensor data.
method Proposes new LSH methods using CP and TT decompositions for Euclidean and cosine similarity.
result Space-efficient and scalable LSH for tensor data.