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Found 5 papers, 1 papers with code
Memory Efficient Experience Replay for Streaming Learning
We find that full rehearsal can eliminate catastrophic forgetting in a variety of streaming learning settings, with ExStream performing well using far less memory and computation.
Compassionately Conservative Balanced Cuts for Image Segmentation
The Normalized Cut (NCut) objective function, widely used in data clustering and image segmentation, quantifies the cost of graph partitioning in a way that biases clusters or segments that are balanced towards having lower values than unbalanced partitionings.
Efficiently Computing Piecewise Flat Embeddings for Data Clustering and Image Segmentation
Image segmentation is a popular area of research in computer vision that has many applications in automated image processing.
Multiple-View Spectral Clustering for Group-wise Functional Community Detection
Functional connectivity analysis yields powerful insights into our understanding of the human brain.
Semi-Supervised Normalized Cuts for Image Segmentation
Since its introduction as a powerful graph-based method for image segmentation, the Normalized Cuts (NCuts) algorithm has been generalized to incorporate expert knowledge about how certain pixels or regions should be grouped, or how the resulting segmentation should be biased to be correlated with priors.
