Journal: PeerJ Computer Science

Article Title: Linguacodus: a synergistic framework for transformative code generation in machine learning pipelines

doi: 10.7717/peerj-cs.2328

Figure Lengend Snippet: Competitions information.

Article Snippet: “Stable diffusion—image to prompts” ( ) , Kaggle , Images , Mean cosine similarity , The competition aims to develop a model that can predict the text prompt that corresponds to a given generated image, challenging the understanding of the relationship between text prompts and images in text-to-image models. The goal is to create embeddings for predicted prompts, ensuring robust prompt similarity assessment, and exploring prompt engineering in the context of image generation..

Techniques: Generated, Diffusion-based Assay

Journal: PeerJ Computer Science

Article Title: Linguacodus: a synergistic framework for transformative code generation in machine learning pipelines

doi: 10.7717/peerj-cs.2328

Figure Lengend Snippet: An automatically chosen and improved with multi-agent LLM best sample instruction generated for “Stable Diffusion—Image to Prompts” competition by fine-tuned Llama 2.

Article Snippet: “Stable diffusion—image to prompts” ( ) , Kaggle , Images , Mean cosine similarity , The competition aims to develop a model that can predict the text prompt that corresponds to a given generated image, challenging the understanding of the relationship between text prompts and images in text-to-image models. The goal is to create embeddings for predicted prompts, ensuring robust prompt similarity assessment, and exploring prompt engineering in the context of image generation..

Techniques: Generated, Diffusion-based Assay

Journal: IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision

Article Title: PathLDM: Text conditioned Latent Diffusion Model for Histopathology

doi: 10.1109/wacv57701.2024.00510

Figure Lengend Snippet: Comparison of reconstruction quality of VAE used in Stable Diffusion, medfusion and PathLDM, evaluated on TCGA-BRCA. The input image size is 256× 256×3. A smaller downsampling factor (f) significantly boosts the SSIM.

Article Snippet: VAE Latent size f SSIM ↑ MSE ↓ Stable Diffusion 32x32x4 8 0.874 25.795 Medfusion 32x32x8 8 0.891 21.401 Ours 64x64x3 4 0.961 11.503 Open in a separate window Comparison of reconstruction quality of VAE used in Stable Diffusion, medfusion and PathLDM, evaluated on TCGA-BRCA.

Techniques: Comparison, Diffusion-based Assay

Journal: IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision

Article Title: PathLDM: Text conditioned Latent Diffusion Model for Histopathology

doi: 10.1109/wacv57701.2024.00510

Figure Lengend Snippet: Effect of latent size on the reconstruction quality of Satellite Images from Deepglobe [ 6 ] dataset. f: downsampling factor. The choice of VAE is significant even in diverse scenarios containing small objects.

Article Snippet: VAE Latent size f SSIM ↑ MSE ↓ Stable Diffusion 32x32x4 8 0.874 25.795 Medfusion 32x32x8 8 0.891 21.401 Ours 64x64x3 4 0.961 11.503 Open in a separate window Comparison of reconstruction quality of VAE used in Stable Diffusion, medfusion and PathLDM, evaluated on TCGA-BRCA.

Techniques: