17 citations · 28 across the 8 of their papers we have counts for
9 papers
Segmentation and Smoothing Affect Explanation Quality More Than the Choice of Perturbation-based XAI Method for Image Explanations
Gustav Grund Pihlgren, Kary Främling
Perturbation-based post-hoc image explanation methods are commonly used to explain image prediction models. These methods perturb parts of the input to measure how those parts affe…
Deep Perceptual Similarity is Adaptable to Ambiguous Contexts
Gustav Grund Pihlgren, Fredrik Sandin, Marcus Liwicki
The concept of image similarity is ambiguous, and images can be similar in one context and not in another. This ambiguity motivates the creation of metrics for specific contexts. T…
A Systematic Performance Analysis of Deep Perceptual Loss Networks: Breaking Transfer Learning Conventions
Gustav Grund Pihlgren, Konstantina Nikolaidou, Prakash Chandra Chhipa +4
In recent years, deep perceptual loss has been widely and successfully used to train machine learning models for many computer vision tasks, including image synthesis, segmentation…
Identifying and Mitigating Flaws of Deep Perceptual Similarity Metrics
Oskar Sjögren, Gustav Grund Pihlgren, Fredrik Sandin +1
Measuring the similarity of images is a fundamental problem to computer vision for which no universal solution exists. While simple metrics such as the pixel-wise L2-norm have been…
Magnification Prior: A Self-Supervised Method for Learning Representations on Breast Cancer Histopathological Images
Prakash Chandra Chhipa, Richa Upadhyay, Gustav Grund Pihlgren +3
This work presents a novel self-supervised pre-training method to learn efficient representations without labels on histopathology medical images utilizing magnification factors. O…
VidHarm: A Clip Based Dataset for Harmful Content Detection
Johan Edstedt, Amanda Berg, Michael Felsberg +4
Automatically identifying harmful content in video is an important task with a wide range of applications. However, there is a lack of professionally labeled open datasets availabl…
Pretraining Image Encoders without Reconstruction via Feature Prediction Loss
Gustav Grund Pihlgren, Fredrik Sandin, Marcus Liwicki
This work investigates three methods for calculating loss for autoencoder-based pretraining of image encoders: The commonly used reconstruction loss, the more recently introduced d…
Improving Image Autoencoder Embeddings with Perceptual Loss
Gustav Grund Pihlgren, Fredrik Sandin, Marcus Liwicki
Autoencoders are commonly trained using element-wise loss. However, element-wise loss disregards high-level structures in the image which can lead to embeddings that disregard them…
Subword Semantic Hashing for Intent Classification on Small Datasets
Kumar Shridhar, Ayushman Dash, Amit Sahu +6
In this paper, we introduce the use of Semantic Hashing as embedding for the task of Intent Classification and achieve state-of-the-art performance on three frequently used benchma…