HASTE enables training-free dynamic compression of pre-trained CNNs by patch-wise LSH-based merging of redundant channels, reporting 46.2% FLOPs reduction on ResNet34 CIFAR-10 with 1.25% accuracy drop.
In: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp
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FreeStory reformulates character consistency as entity-grounded feature reuse for free-form prompts, introduces FreeStoryBench, and reports stronger consistency than baselines among training-free methods.
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HASTE: A Framework for Training-Free, Dynamic, and Steerable Compression of Pre-Trained Convolutional Neural Networks
HASTE enables training-free dynamic compression of pre-trained CNNs by patch-wise LSH-based merging of redundant channels, reporting 46.2% FLOPs reduction on ResNet34 CIFAR-10 with 1.25% accuracy drop.
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FreeStory: Training-Free Character Consistency for Free-Form Visual Storytelling
FreeStory reformulates character consistency as entity-grounded feature reuse for free-form prompts, introduces FreeStoryBench, and reports stronger consistency than baselines among training-free methods.