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Found 5 papers, 0 papers with code
Efficiently Distilling LLMs for Edge Applications
Supernet training of LLMs is of great interest in industrial applications as it confers the ability to produce a palette of smaller models at constant cost, regardless of the number of models (of different size / latency) produced.
Towards A Conceptually Simple Defensive Approach for Few-shot classifiers Against Adversarial Support Samples
In this work, we aim to close this gap by studying a conceptually simple approach to defend few-shot classifiers against adversarial attacks.
Detection of Adversarial Supports in Few-shot Classifiers Using Self-Similarity and Filtering
In this work, we propose a detection strategy to identify adversarial support sets, aimed at destroying the understanding of a few-shot classifier for a certain class.
Toward Scalable and Unified Example-based Explanation and Outlier Detection
We compare performances in terms of the classification, explanation quality, and outlier detection of our proposed network with other baselines.
Simple and Effective Prevention of Mode Collapse in Deep One-Class Classification
However, deep SVDD suffers from hypersphere collapse -- also known as mode collapse, if the architecture of the model does not comply with certain architectural constraints, e. g. the removal of bias terms.
