Cosine-similarity-based hard negative selection during contrastive fine-tuning makes LLM embeddings separate malware families more cleanly, improving few-shot multimodal malware classification accuracy by 11 to 21 percentage points on two datasets.
Enhancing information maximization with distance-aware contrastive learning for source-free cross-domain few- shot learning
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Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings
Cosine-similarity-based hard negative selection during contrastive fine-tuning makes LLM embeddings separate malware families more cleanly, improving few-shot multimodal malware classification accuracy by 11 to 21 percentage points on two datasets.