Loss reweighting is cast as an inverse problem that dynamically infers class weights to equalize per-class average losses under the Neural Collapse simplex ETF target.
Following the notation we defined before, a sample(xi,c, yi,c) from class c, let h(xi,c)∈R p denote the last-layer feature, and let p(xi,c;W) = p1(xi,c;W)
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Rethinking Loss Reweighting for Imbalance Learning as an Inverse Problem: A Neural Collapse Point of View
Loss reweighting is cast as an inverse problem that dynamically infers class weights to equalize per-class average losses under the Neural Collapse simplex ETF target.