Author

Dharmender Saini

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Open access Jul 2026

Adaptive test-time augmentation via KL-regularized reinforcement learning for robust visual inference.

Deep neural networks often suffer significant accuracy degradation when exposed to real-world image corruptions and distribution shifts. To overcome the limitations of fixed, input-agnostic test-time augmentation (TTA), an adaptive framework is proposed that learns per-sample transformations via reinforcement learning....

T. Mittal, A. Dubey, Dharmender Saini et al. · Scientific Reports · 0 citations