Search Results for author:
Found 10 papers, 2 papers with code
Explainable concept mappings of MRI: Revealing the mechanisms underlying deep learning-based brain disease classification
Using quantitative R2* maps we separated Alzheimer's patients (n=117) from normal controls (n=219) by using a convolutional neural network and systematically investigated the learned concepts using Concept Relevance Propagation and compared these results to a conventional region of interest-based analysis.
Reactive Model Correction: Mitigating Harm to Task-Relevant Features via Conditional Bias Suppression
Deep Neural Networks are prone to learning and relying on spurious correlations in the training data, which, for high-risk applications, can have fatal consequences.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
From Hope to Safety: Unlearning Biases of Deep Models via Gradient Penalization in Latent Space
Deep Neural Networks are prone to learning spurious correlations embedded in the training data, leading to potentially biased predictions.
Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models
To tackle this problem, we propose Reveal to Revise (R2R), a framework entailing the entire eXplainable Artificial Intelligence (XAI) life cycle, enabling practitioners to iteratively identify, mitigate, and (re-)evaluate spurious model behavior with a minimal amount of human interaction.
Optimizing Explanations by Network Canonization and Hyperparameter Search
We further suggest a XAI evaluation framework with which we quantify and compare the effect sof model canonization for various XAI methods in image classification tasks on the Pascal-VOC and ILSVRC2017 datasets, as well as for Visual Question Answering using CLEVR-XAI.
Explainable Artificial Intelligence (XAI)
Image Classification
+2
Navigating Neural Space: Revisiting Concept Activation Vectors to Overcome Directional Divergence
With a growing interest in understanding neural network prediction strategies, Concept Activation Vectors (CAVs) have emerged as a popular tool for modeling human-understandable concepts in the latent space.
Multimodal Prototypical Networks for Few-shot Learning
Although providing exceptional results for many computer vision tasks, state-of-the-art deep learning algorithms catastrophically struggle in low data scenarios.
Low-Shot Learning from Imaginary 3D Model
Since the advent of deep learning, neural networks have demonstrated remarkable results in many visual recognition tasks, constantly pushing the limits.
Self Paced Adversarial Training for Multimodal Few-shot Learning
State-of-the-art deep learning algorithms yield remarkable results in many visual recognition tasks.
Cross-modal Hallucination for Few-shot Fine-grained Recognition
State-of-the-art deep learning algorithms generally require large amounts of data for model training.
