Dimensions overall score 4.0

GitHub Code Pulse

No public code linked for this paper yet.

• The effective margin of many triplets often exceeds the predefined value of μ during training in Siamese Networks.technical 90%
• Fixing the margin throughout training may limit the learning process in Siamese Networks.technical 90%
• A margin scheduler that adjusts the margin (μ) based on the proportion of easy triplets observed at each epoch can maintain training difficulty over time.method 90%
• The proposed margin scheduling strategy leads to improved performance compared to a constant margin and a monotonically increasing margin scheme.result 90%
• Experimental results on four different datasets (LFW, CelebA, CUB-200-2011, and Stanford Cars) show consistent gains in verification performance with the proposed margin scheduler.result 90%
• The DAMS scheduler implicitly accounts for factors such as the number of classes and embedding dimensionality, reducing the risk of enforcing incompatible margins.technical 80%
• The proposed margin scheduler (DAMS) achieved higher Recall@1 scores than the Constant margin strategy on LFW (0.395 vs 0.273) and CelebA (0.217 vs 0.137).result 90%

Competitive landscape

Competitor map is still being generated for this paper. Enable generation or check back soon.

Startup potential card