A Generative Framework for Zero-Shot Learning with Adversarial Domain Adaptation

We present a domain adaptation based generative framework for zero-shot learning. Our framework addresses the problem of domain shift between the seen and unseen class distributions in zero-shot learning and minimizes the shift by developing a generative model trained via adversarial domain adaptation... (read more)

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Results from the Paper


 Ranked #1 on Zero-Shot Learning on CUB-200 - 0-Shot Learning (using extra training data)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK USES EXTRA
TRAINING DATA
RESULT BENCHMARK
Zero-Shot Learning CUB-200 - 0-Shot Learning zsl_ADA Average Per-Class Accuracy 70.9 # 1

Methods used in the Paper


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