Efrat R.

Responsibilities:

• CLIP Input Analysis and Pipeline Mapping: Investigated CLIP’s input structure and internal pipeline to determine how textual and visual embeddings are processed and whether the generator must run externally or as part of CLIP’s native flow.

• CLIP Variant Evaluation and Deployment: Compared multiple CLIP variants (e.g., ViT-based architectures), selected the optimal version, deployed it on the remote GPU server, and validated its performance for downstream tasks.

• Comparative Model Analysis (SDXL vs. SD 0.1) Authored a detailed comparative analysis of two generative architectures—including training objectives, noise schedulers, latent resolutions, and semantic alignment—and evaluated their suitability for SEM simulation.

• Textual Inversion Evaluation on SDXL: Trained and tested Textual Inversion embeddings on SDXL for SEM-specific conditioning, and evaluated model behavior to detect embedding instability, noise inconsistencies, and edge-artifact degradation.

• Classification Gap Investigation: Analyzed why the classification model achieves near-perfect separation between real and generated SEM images, and authored a technical summary detailing root causes and mitigation strategies.

• Noise & Edge-Artifact Evaluation in Segmentation-Based Generation: Studied the impact of missing noise patterns and edge-artifacts in images generated from segmentation masks, experimented with techniques to simulate realistic SEM noise, and evaluated edge-reduction methods to minimize detectable discrepancies between real and mask-generated outputs.