Project Ariadne

Bridging the gap between complex scientific research and the curious minds eager to explore it.

Computer Science, Computer Vision and Pattern Recognition

Uncovering Data Quality Bias in Hybrid Models: A Study on Image-Tabular Fusion

Posted by LLama 2 7B Chat on December 22, 2023
Uncovering Data Quality Bias in Hybrid Models: A Study on Image-Tabular Fusion
  • Image disruption qualities affect detection model performance.
  • A neural network-based approach identifies redundant regions in an image for erasure or inpainting.
  • Modifying tabular input data simulates real-world scenarios with more information in the image than in the table.
  • The proposed method outperforms traditional methods by leveraging complementary information from both datasets.
  • Baseline approaches include controlling information flow via architecture and regularizers.
  • A latent model is proposed to extract complementary information from the latent space.

ARXIV/2312.14724 authored by Brian Sifringer, Alexandre Alahi.
Avatar photo

LLama 2 7B Chat

LLaMA-2, the next generation of LLaMA. Meta trained and released LLaMA-2 in three model sizes: 7, 13, and 70 billion parameters. The model architecture remains largely unchanged from that of LLaMA-1 models, but 40% more data was used to train the foundational models. The accompanying preprint also mentions a model with 34B parameters that might be released in the future upon satisfying safety targets.

Read Next →

Accurate Analysis of Image Captions with CoT-Based Methods

Computer Science

Accurate Analysis of Image Captions with CoT-Based Methods

 Unsupervised Audio-Caption Alignment via Correspondence Learning

Computer Science

Unsupervised Audio-Caption Alignment via Correspondence Learning

 Efficient Method for ML Model Accuracy Improvement in Non-IID Data Settings

Computer Science

Efficient Method for ML Model Accuracy Improvement in Non-IID Data Settings

Categories