Abstract: The capacity to adapt machine learning models to various contexts, information, and objectives is particularly valuable. In this thesis, I focus on developing Class Conditional Guided Models. These are models that can be adaptively biased towards a class of interest via a conditional input. My primary focus lies in the efficiency of these models. They are constructed to require training only once, with the ability to quickly and conveniently adapt during testing time without necessitating fine-tuning or retraining.
Firstly, I propose RelationVAE, a novel generative model designed for few-shot scenarios, utilizing the prior knowledge of class similarity relationships. RelationVAE is designed to condition on the embeddings of the neighbor classes (i.e. classes with similarity relationships), to generate more reliable samples by making them more similar to the neighbor class. This enables adaptation of the generative model to the provided prior knowledge about class relationships.
As a second focus, I introduce scGAN, a shadow segmentation technique that enables adaptation to varying shadow distributions in different testing environments. scGAN is designed to condition on a sensitivity parameter, a scalar, to control the amount of the shadow detected. In the testing phase, the parameter is set to appropriate values, allowing the model to quickly adapt to specific test environments.
In my third contribution, I propose S-SEG, a methodology for fine-grained counting allowing adaptation to different granularities of fine-grained classes. In fine-grained problems, the distinction between classes is subtle and inconsistent across images, leading to variations in the granularity of the target class from one image to another. S-SEG is designed to be conditioned on an additional input, the sensitivity parameter, to control the granularities of the target class during inference.
My fourth contribution is a text-to-image synthesis method which allows controlling the number of the generated objects of a target class. I propose to generate an intermediate condition, the density map, which reflects the number of objects, together with their layout. This intermediate condition is used to effectively guide the generative model to generate objects with accurate counts.
Speaker: Vu Nguyen
Zoom: https://stonybrook.zoom.us/j/97114455337?pwd=Z4rB9dWcstlahUIs8PRrvQ9b2ZK2Df.1Meeting ID: 971 1445 5337
Passcode: 272300
