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Computing & AI · Source-linked note

Once-for-All progressive shrinking for deployable subnetworks

Trains a shared-weight network progressively over kernel size, depth, width and resolution, then selects subnetworks for multiple deployment constraints without separately training each from scratch.

What is recorded here?

Trains a shared-weight network progressively over kernel size, depth, width and resolution, then selects subnetworks for multiple deployment constraints without separately training each from scratch.

Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.

Scientific scope

The O(1) training claim is relative to deployment-scenario count after one expensive supported-space training; per-device search/evaluation and hardware measurements still cost resources. Accuracy equivalence to independent training is empirical, not a theorem over all 10^19 subnetworks.

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People and systems

Han Cai; Chuang Gan; Song Han

Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.

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English catalogue narrative

Once-for-All progressive shrinking for deployable subnetworks

Trains a shared-weight network progressively over kernel size, depth, width and resolution, then selects subnetworks for multiple deployment constraints without separately training each from scratch.

Scientific scope

The O(1) training claim is relative to deployment-scenario count after one expensive supported-space training; per-device search/evaluation and hardware measurements still cost resources. Accuracy equivalence to independent training is empirical, not a theorem over all 10^19 subnetworks.

Reading status

This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.

People and systems

Han Cai; Chuang Gan; Song Han

Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.

Original sources