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HASTE: A Framework for Training-Free, Dynamic, and Steerable Compression of Pre-Trained Convolutional Neural Networks
Lukas Meiner, Jens Mehnert, Alexandru Paul Condurache
Deploying large convolutional neural networks (CNNs) on resource-constrained devices is challenging due to their high computational cost. While dynamic execution methods are promis…
HTTM: Head-wise Temporal Token Merging for Faster VGGT
Weitian Wang, Lukas Meiner, Rai Shubham +2
The Visual Geometry Grounded Transformer (VGGT) marks a significant leap forward in 3D scene reconstruction, as it is the first model that directly infers all key 3D attributes (ca…
PROM: Prioritize Reduction of Multiplications Over Lower Bit-Widths for Efficient CNNs
Lukas Meiner, Jens Mehnert, Alexandru Paul Condurache
Convolutional neural networks (CNNs) are crucial for computer vision tasks on resource-constrained devices. Quantization effectively compresses these models, reducing storage size…
Data-Free Dynamic Compression of CNNs for Tractable Efficiency
Lukas Meiner, Jens Mehnert, Alexandru Paul Condurache
To reduce the computational cost of convolutional neural networks (CNNs) on resource-constrained devices, structured pruning approaches have shown promise in lowering floating-poin…