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Algorithm Research Roadmap Template (Deep Learning)

Five-phase roadmap for algorithm papers: problem definition, data, method, experiments and conclusion, including baseline reproduction, the proposed model, ablation studies and sensitivity analysis. Free PNG, Word and PowerPoint export.

Conclusion

Analysis of results

Conclusions and future work

Experiments

Comparison with baselines

Ablation study

Sensitivity analysis

Method

Reproduce baselines

Proposed model

Loss function and training strategy

Data

Data collection

Cleaning and labelling

Train / validation / test split

Problem definition

Task and evaluation metrics

Survey existing methods and gaps

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When to use it

Master's theses and proposals in computer science, automation and AI. Compared with the general version, this one lists the steps reviewers of algorithm papers care about most: baseline reproduction, the proposed improvement, comparison, ablation and sensitivity analysis.

Making it yours

  • Replace "Proposed model" with the actual method name, such as "Attention-based XX network".
  • Put metrics inside the node: A[Task and evaluation metrics<br/>mAP / F1].
  • For public datasets, change "Data collection" to "Select the XX dataset" and drop the labelling step.
  • Add a "Deployment and application validation" phase before the conclusion if the work is applied.

Mermaid code

flowchart TB
    subgraph P1[Problem definition]
        direction LR
        A[Task and evaluation metrics] --> B[Survey existing methods and gaps]
    end
    subgraph P2[Data]
        direction LR
        C[Data collection] --> D[Cleaning and labelling] --> E[Train / validation / test split]
    end
    subgraph P3[Method]
        direction LR
        F[Reproduce baselines] --> G[Proposed model] --> H[Loss function and training strategy]
    end
    subgraph P4[Experiments]
        direction LR
        I[Comparison with baselines] --> J[Ablation study] --> K[Sensitivity analysis]
    end
    subgraph P5[Conclusion]
        direction LR
        L[Analysis of results] --> M[Conclusions and future work]
    end
    P1 --> P2 --> P3 --> P4 --> P5

Ask an AI for one with this structure

Send the prompt below to ChatGPT or Claude, then paste the whole reply into the editor; the code inside is detected automatically.

Draw a deep learning research roadmap in Mermaid flowchart TB syntax with five subgraph phases: Problem definition (task and evaluation metrics → survey existing methods and gaps), Data (data collection → cleaning and labelling → train/validation/test split), Method (reproduce baselines → proposed model → loss function and training strategy), Experiments (comparison with baselines → ablation study → sensitivity analysis), Conclusion (analysis of results → conclusions and future work). Add direction LR inside each subgraph so the steps run horizontally, and connect the phases by their subgraph ids (for example S1 --> S2). Output only the mermaid code block.

FAQ

Can nodes contain formulas?

Not really. Node text is plain text; subscripts and Greek letters can only be approximated with Unicode characters (α, β, x²). Keep formulas in the text and use step names in the figure.

How do I show two experiments running in parallel?

Put them in the same subgraph without connecting them to each other; they are placed side by side.

Is the exported figure sharp enough for a thesis?

Export the PNG at 3x, or insert the SVG export into Word; both stay sharp in print and when zoomed.

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Updated 2026-09-05