Ablation Study on Weighting Coefficient for Class-Incremental Learning

A 5.5 KB Excel file containing results from an ablation study on a weighting coefficient, authored by Jianye Gu and last updated on 2026-05-07. The data supports research on the DGASA method for Class-Incremental Learning, which aims to mitigate catastrophic forgetting in models learning new categories.

Use Cases

• Analyzing the impact of a weighting coefficient on model performance based on the described ablation study.
• Benchmarking continual learning methods based on the described classification accuracy and forgetting resistance metrics.
• Reproducing or validating the DGASA method's subspace alignment technique based on the experimental results.

Strengths

• File size is precisely 5.5 KB, indicating a compact and focused dataset.
• Dataset has a clear CC-BY-4.0 license for open use.
• Last update timestamp is explicitly provided as 2026-05-07 17:41:41.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

figshare

Collection Method

Likely contains experimental results from the described ablation study.

Time Range

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Freshness

Last updated 2026-05-07 17:41:41; freshness should be verified.

Geography

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