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NASNet Scheduled DropPath

Algorithms AIMech:NASNetScheduledDropPath DRAFT

A NASNet training regularizer that stochastically suppresses operation-bearing edges within convolutional cells using a drop probability that increases linearly over the course of training.

Identity Notes & Concept Scope This record denotes the original NASNet temporal operation-edge dropping procedure, distinct from the search controller, complete network, FractalNet local/global protocol and a fixed depth-survival schedule. Its unspecified numeric and implementation controls remain open. No bare DropPath alias is assigned. Reuse owner 209 alongside FractalNet DropPath, retaining the old source pointer only as historical discovery context. Current creation assessment uses the actual merged round 336 base d76936e4e77768d08b0cdd7f505ed34c9f50a5f0 with 745 native and 442 pinned AIO records. All 1,187 identity/hash rows were compared to retained research snapshots with no unexpected change. Four ignored/hidden-inclusive searches returned 191, 190, 526 and 575 occurrences; root read all 26 distinct matching contents, including 18 newly encountered context strings. Normalized ID/name/alias/scoped-synonym probes had no collision. The full 908-main and 393-nested frontier was queried and relevant current owners read. These are bounded current identity findings, not global completeness or exhaustion.

Claims & Grounded Evidence

6 Claims

Scope #named-cell-training-regularizer

ScheduledDropPath is the named modification of path dropping introduced for training NASNet networks. It acts within a chosen cell architecture while network parameters are trained. It is not the architecture-search controller, the NASNet search space, a complete NASNet architecture or a particular trained classifier.

supports Source: Learning Transferable Architectures for Scalable Image Recognition · Locator: Section 4, final paragraph, PDF p. 4 / printed p. 8700; Appendix A.5, PDF p. 11 / printed p. 8707. Appendix A.2–A.3 on the same PDF page separately describes the controller and architecture search.

Mechanism #operation-bearing-cell-edges

The paper identifies a dropped path as an edge containing an operation box in a NASNet cell. Figure 4 shows primitive operation branches feeding combination operations and the cell output. This defines a structured graph-edge masking unit rather than an individual scalar activation or an entire network column.

supports Source: Learning Transferable Architectures for Scalable Image Recognition · Locator: Appendix A.5, PDF p. 11 / printed p. 8707, explicit reference to operation-box edges; Figure 4 and caption, PDF p. 5 / printed p. 8701.

Mechanism #linear-training-time-drop-schedule

For the named schedule, increase the probability of dropping a path linearly over the course of training. The comparator described in the same passage uses a fixed probability. The varying axis here is training progress, not merely a different constant for each layer depth. The passage identifies the schedule family without specifying its numeric endpoints or a complete indexed implementation formula.

supports Source: Learning Transferable Architectures for Scalable Image Recognition · Locator: Section 4, final paragraph, PDF p. 4 / printed p. 8700; Appendix A.5, final sentences, PDF p. 11 / printed p. 8707.

Mechanism #paper-level-test-scaling-convention

Appendix A.5 describes stochastic path dropping during training and test-time scaling of a path by its training keep probability, by analogy with the cited dropping methods. For ScheduledDropPath's changing probability, the passage does not identify which time-indexed keep probability is used at inference. This retains the paper-level convention without replacing it with an assumed inverted-training implementation or an exact full-network expectation rule.

supports Source: Learning Transferable Architectures for Scalable Image Recognition · Locator: Appendix A.5, middle and final sentences, PDF p. 11 / printed p. 8707.

Scope #distinct-from-ordinary-output-dropout

The cell-path procedure is distinct from the ordinary dropout also present in the reported training configuration. In particular, the ImageNet protocol applies dropout with probability 0.5 to the final softmax matrix. That separate output-stage setting does not specify the ScheduledDropPath endpoint, mask-sharing rule or cell-path probability.

supports Source: Learning Transferable Architectures for Scalable Image Recognition · Locator: Appendix A.5, PDF p. 11 / printed p. 8707; Appendix A.7, final sentences, PDF p. 12 / printed p. 8708.

Scope #host-and-empirical-protocol-boundary

The source studies the regularizer in NASNet image-classification protocols for CIFAR and ImageNet, together with chosen cell structures, model sizes, optimizers, learning-rate schedules and other training choices. The described NASNet host does not add residual connections between convolutional cells. The regularizer's identity is the time-varying cell-edge dropping procedure; it does not inherit every architecture-search efficiency or whole-model accuracy claim in the paper.

supports Source: Learning Transferable Architectures for Scalable Image Recognition · Locator: Section 4, PDF p. 4 / printed p. 8700; Section 4.2, residual-connection statement, PDF p. 5 / printed p. 8701; Appendix A.5–A.7, PDF pp. 11–12 / printed pp. 8707–8708.

⚠️ Curated Limitations & Negative Results

The inspected method passages do not specify schedule endpoints, an exact training-progress index, clipping, per-layer depth adjustment, or batch-versus-example mask sharing. A linear increase must use valid probabilities, but no later software default or complete formula is silently supplied as original-paper evidence.
Appendix A.5 refers generically to operation-box edges and gives no explicit identity-operation exception, independence rule or safeguard for every path being dropped. The paper-level description should not be completed by borrowing FractalNet's nonempty-join rule. A pinned implementation could resolve its own conventions, but none was inspected here.
The test-time scaling discussion is not a fully indexed formula for a changing schedule. It does not choose a final, average or other effective keep probability, and it does not establish that deterministic scaling gives the exact expected prediction of the nonlinear network. Training-time inverted scaling is not inferred from this passage.
Suppressing an operation's current contribution does not imply that all of its parameters or state remain unchanged under momentum, weight decay, shared use or running statistics. The paper-level identity does not certify implementation-level computation savings, exact gradient semantics or a particular mask-sharing scheme.
The temporal schedule is distinct from a fixed block-depth survival schedule, the local/global FractalNet protocol, and a schedule that orders training examples. Shared special cases or use of the generic DropPath name do not make the complete procedures exact aliases. No bare DropPath alias is assigned.
The evidence concerns the reported NASNet protocols, which include other regularizers, augmentation and architecture choices. No isolated numerical benefit, universal superiority, convergence theorem, causal-ablation result or benchmark reproduction is asserted for ScheduledDropPath alone. The procedure is not the NASNet search algorithm or the trained model family.
The primary evidence is the official CVPR 2018 PDF with 14 pages including appendices. The retained research read text on PDF pp. 1–5 and 11–12 and selected visuals, not all 14 pages or author code. Other arXiv versions and later implementations are not assumed identical. The named schedule is identifiable at this scope while strict replication still needs the missing control details.

Artificial Intelligence Ontology (AIO) Grounding

Mapped AIO Classes

AIO Target Class	Mapping Relation	Mapping Rationale
`AIO:TrainingStrategy` TrainingStrategy	related_match	ScheduledDropPath changes the distribution of participating network paths during training, supporting a qualified relationship to TrainingStrategy. The pinned target describes a broad training category using a preprocessing genus, not this particular temporal graph-mask schedule. This related match asserts neither equivalence nor subclass membership. It does not identify the procedure with a network architecture, a penalty added to the loss or a curriculum over training examples.

Evidence Sources & Primary Literature

1 Source

Source ID	Publication / Document	Type / Access	Reference & Checksum
`zoph2018-cvpr`	Learning Transferable Architectures for Scalable Image Recognition Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le	primary paper full_text	ID: CVF:Zoph_Learning_Transferable_Architectures_CVPR_2018 sha256:0a0c3e5396d7…

Curation Provenance

Curator: Codex

Method: ai_assisted

Model: GPT-6

Created: 2026-10-15T09:02:41Z

Updated: 2026-10-15T09:02:41Z

Source File: data/curated/NASNetScheduledDropPath.yaml