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Found 3 papers, 2 papers with code
A Training-Free Defense Framework for Robust Learned Image Compression
We study the robustness of learned image compression models against adversarial attacks and present a training-free defense technique based on simple image transform functions.
Unsupervised Pre-Training For Data-Efficient Text-to-Speech On Low Resource Languages
We empirically demonstrate the effectiveness of our proposed method in low-resource language scenarios, achieving outstanding performance compared to competing methods.
Variable-Rate Deep Image Compression through Spatially-Adaptive Feature Transform
In addition, the proposed framework allows us to perform task-aware image compressions for various tasks, e. g., classification, by efficiently estimating optimized quality maps specific to target tasks for our encoding network.
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