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Interactive Dual Generative Adversarial Networks for Image Captioning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add retrieval-based generator Gθ2 (ablation) | IDGAN · MSCOCO Karpathy test | CIDEr: 121.7 -> 123.5 | Helped |
| add retrieval-based discriminator Dφ2 (ablation) | IDGAN · MSCOCO Karpathy test | CIDEr: 121.3 -> 123.5 | Helped |
| add generation discriminator Dφ1 (ablation) | IDGAN · MSCOCO Karpathy test | CIDEr: 122.1 -> 123.5 | Helped |
| add copy mechanism from retrieved guidance captions (ablation) | IDGAN · MSCOCO Karpathy test | CIDEr: 122.7 -> 123.5 | Helped |
| add retrieval-based discriminator Dφ2 (ablation) | IDGAN · MSCOCO Karpathy test | BLEU-4: 36.9 -> 38.5 | Helped |
| add retrieval-based generator Gθ2 (ablation) | IDGAN · MSCOCO Karpathy test | BLEU-4: 36.8 -> 38.5 | Helped |
| add copy mechanism (ablation) | IDGAN · MSCOCO Karpathy test | BLEU-4: 38.2 -> 38.5 | Helped |
| IDGAN dual adversarial retrieval-generation vs TopDown backbone | TopDown · MSCOCO Karpathy test | CIDEr: 120.1 -> 123.5 | Helped |
| IDGAN vs TopDown human evaluation | TopDown · MSCOCO human eval | Informativeness: 3.25 -> 3.32 | Helped |
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- Improving Image Captioning with Conditional Generative Adversarial Nets (AAAI 2019 · similarity 0.91)
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