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Found 8 papers, 1 papers with code
Verifix: Post-Training Correction to Improve Label Noise Robustness with Verified Samples
We introduce Verifix, a novel Singular Value Decomposition (SVD) based algorithm that leverages a small, verified dataset to correct the model weights using a single update.
Unveiling Privacy, Memorization, and Input Curvature Links
Second, we present a novel insight showing that input loss curvature is upper-bounded by the differential privacy parameter.
Memorization Through the Lens of Curvature of Loss Function Around Samples
Second, we inject corrupted samples which are memorized by the network, and show that these are learned with high curvature.
Homogenizing Non-IID datasets via In-Distribution Knowledge Distillation for Decentralized Learning
The goal of IDKD is to homogenize the data distribution across the nodes.
Norm-Scaling for Out-of-Distribution Detection
Therefore, applying a single threshold for all classes is not ideal since the same similarity score represents different uncertainties for different classes.
Intra-class Mixup for Out-of-Distribution Detection
This reduces the separability of in-distribution data from OoD data.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Exploring Vicinal Risk Minimization for Lightweight Out-of-Distribution Detection
We utilize mixup in two ways to implement Vicinal Risk Minimization.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
TREND: Transferability based Robust ENsemble Design
In this work, we study the effect of network architecture, initialization, optimizer, input, weight and activation quantization on transferability of adversarial samples.
