lm✳LOTFI ABDELKRIM MECHARBATPHD · MACHINE LEARNING
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PROJECT / AAAI 2027 · UNDER REVIEWLOTFI ABDELKRIM MECHARBAT & COLLABORATORS

CarveVFM

Carving Specialists Out of Generalists

Extract a compact, dense encoder by jointly considering capacity allocation, removal structure, and how well removed behavior can be recovered.

Figure 1 · Accuracy under different layer-wise allocations
METHOD & EXPERIMENTS

The manuscript evaluates models from DINOv3 and MetaCLIP-2 across twelve benchmarks. The visualization below isolates capacity allocation; the full method also searches operators and performs recovery.

1.3–2.0×higher GPU throughput
than the uncompressed model
Reported in the manuscript; see the paper for evaluation settings.
Figure 2 · Full method overview
AN INTERACTIVE EXPLANATION

Method overview

CARVEVFM / CONCEPT DEMO

Layer capacity allocation

Uniform compression gives every layer the same width. Task-aware allocation preserves more capacity in layers that matter to the task.

Task-aware allocation Uniform allocation

The paper jointly searches capacity allocation, removal operators, and recoverability. The chart isolates the allocation idea.

Method & experiments ↗
Capacity by layerRETAINED WIDTH / LAYER

Schematic allocation under a shared capacity budget. No accuracy or latency is predicted.

RELATED WORK

Related work

Channel-Remix ↗
CONTACT

Contact

You can email me about my research or a possible collaboration.

Paper citation

BibTeX citation

Project illustration

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