DiffSlack introduces learnable slack variables and a damped Gauss-Newton projection to create a differentiable layer that enforces hard nonlinear inequality constraints in neural network outputs.
Constraint guided gradient descent: Training with inequal- ity constraints with applications in regression and semantic segmentation
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DiffSlack: Learning under Nonlinear Inequality Constraints via Learnable Slack Variables
DiffSlack introduces learnable slack variables and a damped Gauss-Newton projection to create a differentiable layer that enforces hard nonlinear inequality constraints in neural network outputs.