Search Results for author:
Found 7 papers, 4 papers with code
BBAM: Bounding Box Attribution Map for Weakly Supervised Semantic and Instance Segmentation
Weakly supervised segmentation methods using bounding box annotations focus on obtaining a pixel-level mask from each box containing an object.
Removing Undesirable Feature Contributions Using Out-of-Distribution Data
Herein, we propose a data augmentation method to improve generalization in both adversarial and standard learning by using out-of-distribution (OOD) data that are devoid of the abovementioned issues.
Interpretation of NLP models through input marginalization
To demystify the "black box" property of deep neural networks for natural language processing (NLP), several methods have been proposed to interpret their predictions by measuring the change in prediction probability after erasing each token of an input.
Information-Theoretic Visual Explanation for Black-Box Classifiers
IG map provides a class-independent answer to "How informative is each pixel?
iCaps: An Interpretable Classifier via Disentangled Capsule Networks
We propose an interpretable Capsule Network, iCaps, for image classification.
Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentation
In this paper, we address the problem of image anomaly detection and segmentation.
Ranked #8 on
Anomaly Detection
on BTAD
(using extra training data)
Energy-Based Sequence GANs for Recommendation and Their Connection to Imitation Learning
Recommender systems aim to find an accurate and efficient mapping from historic data of user-preferred items to a new item that is to be liked by a user.
