SPACO is a new single-loop stochastic algorithm for stochastic nonconvex-concave minimax problems with nonlinear convex coupled constraints that uses penalty smoothing and provides non-asymptotic complexity bounds plus stationarity analysis.
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2026 3verdicts
UNVERDICTED 3representative citing papers
MIND uses sliced Wasserstein distance on Inception features to evaluate generative models, matching FID performance with 10x fewer samples and 100x faster computation while being more robust to moment-matching attacks.
IConFace performs unified reference-aware and no-reference blind face restoration by asymmetrically conditioning identity from references and structure from the degraded image.
citing papers explorer
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A Single-Loop Stochastic Gradient Algorithm for Minimax Optimization with Nonlinear Coupled Constraints
SPACO is a new single-loop stochastic algorithm for stochastic nonconvex-concave minimax problems with nonlinear convex coupled constraints that uses penalty smoothing and provides non-asymptotic complexity bounds plus stationarity analysis.
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MIND: Monge Inception Distance for Generative Models Evaluation
MIND uses sliced Wasserstein distance on Inception features to evaluate generative models, matching FID performance with 10x fewer samples and 100x faster computation while being more robust to moment-matching attacks.
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IConFace: Identity-Structure Asymmetric Conditioning for Unified Reference-Aware Face Restoration
IConFace performs unified reference-aware and no-reference blind face restoration by asymmetrically conditioning identity from references and structure from the degraded image.