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Found 5 papers, 5 papers with code
Attention Calibration for Disentangled Text-to-Image Personalization
However, an intriguing problem persists: Is it possible to capture multiple, novel concepts from one single reference image?
Improving Few-shot Image Generation by Structural Discrimination and Textural Modulation
Furthermore, the frequency awareness of the model is reinforced by encouraging the model to distinguish frequency signals.
Image Synthesis under Limited Data: A Survey and Taxonomy
Despite numerous efforts to enhance training stability and synthesis quality in the limited data scenarios, there is a lack of a systematic survey that provides 1) a clear problem definition, critical challenges, and taxonomy of various tasks; 2) an in-depth analysis on the pros, cons, and remain limitations of existing literature; as well as 3) a thorough discussion on the potential applications and future directions in the field of image synthesis under limited data.
FreGAN: Exploiting Frequency Components for Training GANs under Limited Data
Training GANs under limited data often leads to discriminator overfitting and memorization issues, causing divergent training.
WaveGAN: Frequency-aware GAN for High-Fidelity Few-shot Image Generation
Concretely, we disentangle encoded features into multiple frequency components and perform low-frequency skip connections to preserve outline and structural information.
