Search Results for author:
Found 6 papers, 3 papers with code
Concept Arithmetics for Circumventing Concept Inhibition in Diffusion Models
We use compositional property of diffusion models, which allows to leverage multiple prompts in a single image generation.
Human Evaluation of Text-to-Image Models on a Multi-Task Benchmark
For example, asking a model to generate a varying number of the same object to measure its ability to count or providing a text prompt with several objects that each have a different attribute to identify its ability to match objects and attributes correctly.
Black-box Explanation of Object Detectors via Saliency Maps
We propose D-RISE, a method for generating visual explanations for the predictions of object detectors.
Why do These Match? Explaining the Behavior of Image Similarity Models
Explaining a deep learning model can help users understand its behavior and allow researchers to discern its shortcomings.
Guided Zoom: Questioning Network Evidence for Fine-grained Classification
We propose Guided Zoom, an approach that utilizes spatial grounding of a model's decision to make more informed predictions.
RISE: Randomized Input Sampling for Explanation of Black-box Models
We compare our approach to state-of-the-art importance extraction methods using both an automatic deletion/insertion metric and a pointing metric based on human-annotated object segments.
Explainable Artificial Intelligence (XAI)
Feature Importance
+3
