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Integrated Generalized Zero-Shot Learning for Fine-Grained Classification
In this paper, we propose to explore global and direct attribute-supervised local visual features for both EL and FS categories in an integrated manner for fine-grained GZSL.
Bidirectional Mapping Coupled GAN for Generalized Zero-Shot Learning
Bidirectional mapping-based generalized zero-shot learning (GZSL) methods rely on the quality of synthesized features to recognize seen and unseen data.
Generalized Zero-Shot Learning
Generative Adversarial Network
Adversarial Network with Multiple Classifiers for Open Set Domain Adaptation
The proposed multi-classifier structure introduces a weighting module that evaluates distinctive domain characteristics for assigning the target samples with weights which are more representative to whether they are likely to belong to the known and unknown classes to encourage positive transfers during adversarial training and simultaneously reduces the domain gap between the shared classes of the source and target domains.
Enhanced Transfer Learning with ImageNet Trained Classification Layer
Thus, we hypothesize that the presence of this layer is crucial for growing network depth to adapt better to a new task.
Transfer Learning Using Classification Layer Features of CNN
Although CNNs have gained the ability to transfer learned knowledge from source task to target task by virtue of large annotated datasets but consume huge processing time to fine-tune without GPU.
