Search Results for author:
Found 7 papers, 2 papers with code
PEEB: Part-based Image Classifiers with an Explainable and Editable Language Bottleneck
CLIP-based classifiers rely on the prompt containing a {class name} that is known to the text encoder.
The Impact of Frequency Bands on Acoustic Anomaly Detection of Machines using Deep Learning Based Model
In this paper, we propose a deep learning based model for Acoustic Anomaly Detection of Machines, the task for detecting abnormal machines by analysing the machine sound.
LSTM-based Deep Neural Network With A Focus on Sentence Representation for Sequential Sentence Classification in Medical Scientific Abstracts
For this reason, the role of sentence embedding is crucial for capturing both the semantic information between words in the sentence and the contextual relationship of sentences within the abstract to provide a comprehensive representation for better classification.
Leveraging Habitat Information for Fine-grained Bird Identification
Training CNNs and ViTs with habitat-augmented data results in an improvement of up to +0. 83 and +0. 23 points on NABirds and CUB-200, respectively.
Towards Conceptualization of "Fair Explanation": Disparate Impacts of anti-Asian Hate Speech Explanations on Content Moderators
We apply this method in the context of content moderation of potential hate speech, and its differential impact on Asian vs. non-Asian proxy moderators, across explanation approaches (saliency map and counterfactual explanation).
The Impact of Explanations on Fairness in Human-AI Decision-Making: Protected vs Proxy Features
In this work, we study the effect of the presence of protected and proxy features on participants' perception of model fairness and their ability to improve demographic parity over an AI alone.
ViT-DeiT: An Ensemble Model for Breast Cancer Histopathological Images Classification
Breast cancer is the most common cancer in the world and the second most common type of cancer that causes death in women.
