Pamulapati, Venkata Sai Sudeep, authorBlanchard, Nathaniel, advisorBeveridge, Ross, advisorKing, Emily, committee member2022-08-292022-08-292022https://hdl.handle.net/10217/235560Zero-shot classifiers identify unseen classes — classes not seen during training. Specifically, zero-shot models classify attribute information associated with classes (e.g., a zebra has stripes but a lion does not). Lately, the usage of generative adversarial networks (GAN) for zero-shot learning has significantly improved the recognition accuracy of unseen classes by producing visual features on any class. Here, I investigate how similar visual features obtained from images of a class are to the visual features generated by a GAN. I find that, regardless of metric, both sets of visual features are disjointed. I also fine-tune a ResNet so that it produces visual features that are similar to the visual features generated by a GAN — this is novel because all standard approaches do the opposite: they train the GAN to match the output of the model. I conclude that these experiments emphasize the need to establish a standard input pipeline in zero-shot learning because of the mismatch of generated and real features, as well as the variation in features (and subsequent GAN performance) from different implementations of models such as ResNet-101.born digitalmasters thesesengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.ResNetgenerative adversarial networkszero shot learningText