Search Results for author:
Found 8 papers, 2 papers with code
ANNA”:" Enhanced Language Representation for Question Answering
Pre-trained language models have brought significant improvements in performance in a variety of natural language processing tasks.
Instruction Matters, a Simple yet Effective Task Selection Approach in Instruction Tuning for Specific Tasks
Instruction tuning has shown its ability to not only enhance zero-shot generalization across various tasks but also its effectiveness in improving the performance of specific tasks.
AutoGuide: Automated Generation and Selection of State-Aware Guidelines for Large Language Model Agents
The primary limitation of large language models (LLMs) is their restricted understanding of the world.
ReConPatch : Contrastive Patch Representation Learning for Industrial Anomaly Detection
In this paper, we introduce ReConPatch, which constructs discriminative features for anomaly detection by training a linear modulation of patch features extracted from the pre-trained model.
Ranked #1 on
Anomaly Detection
on MVTec AD
Significantly Improving Zero-Shot X-ray Pathology Classification via Fine-tuning Pre-trained Image-Text Encoders
However, large-scale and high-quality data to train powerful neural networks are rare in the medical domain as the labeling must be done by qualified experts.
ANNA: Enhanced Language Representation for Question Answering
Pre-trained language models have brought significant improvements in performance in a variety of natural language processing tasks.
L-Verse: Bidirectional Generation Between Image and Text
Unlike other models, BiART can distinguish between image (or text) as a conditional reference and a generation target.
Ranked #1 on
Image Reconstruction
on ImageNet 256x256
Explaining Convolutional Neural Networks through Attribution-Based Input Sampling and Block-Wise Feature Aggregation
In this work, we collect visualization maps from multiple layers of the model based on an attribution-based input sampling technique and aggregate them to reach a fine-grained and complete explanation.
