@generalpha
Prompt
Data selection and cleaning are essential in reducing parameters in a model, especially in neural networks. Selection focuses on relevant and representative samples, reducing dataset size and mitigating overfitting. Cleaning removes noise and inconsistencies, improving data quality and model performance. By selecting informative data, we achieve similar or better results with fewer parameters. Cleaning further simplifies the problem space, reducing dimensionality and improving computational effi
doubles, twins, entangled fingers, Worst Quality, ugly, ugly face, watermarks, undetailed, unrealistic, double limbs, worst hands, worst body, Disfigured, double, twin, dialog, book, multiple fingers, deformed, deformity, ugliness, poorly drawn face, extra_limb, extra limbs, bad hands, wrong hands, poorly drawn hands, messy drawing, cropped head, bad anatomy, lowres, extra digit, fewer digit, worst quality, low quality, jpeg artifacts, watermark, missing fingers, cropped, poorly drawn
11 months ago
Model
SSD-1B
Guidance Scale
7
Dimensions
3328 × 4992
Similar
![[mahematics] In the context of universal approximation, two approaches can achieve similar results but with different parameter requirements. The overall system comprises data, architecture, and a loss function, interconnected by a learning procedure. Responsibilities within the system include acknowledging noisy or biased data, addressing the need for a large number of parameters in the architecture, and overcoming the principal-agent problem in the choice of the loss function.](https://img.stablecog.com/insecure/256w/aHR0cHM6Ly9iLnN0YWJsZWNvZy5jb20vNzE2NTdhMmYtNWI1Ny00MGM5LWFmMzgtODIxNTNjNDA5NmJiLmpwZWc.webp)
