Papers Slice Discovery
“Slice Discovery” 태그가 달린 논문 10편 · 필터 해제
VISLIX: An XAI Framework for Validating Vision Models with Slice Discovery and Analysis
Real-world machine learning models require rigorous evaluation before deployment, especially in safety-critical domains like autonomous driving and surveillance. The evaluation of machine learning models often focuses on…
Autonomous Drivingobject-detectionObject DetectionSlice DiscoveryError Slice Discovery via Manifold Compactness
Despite the great performance of deep learning models in many areas, they still make mistakes and underperform on certain subsets of data, i.e. error slices. Given a trained model, it is important to identify its semanti…
Slice DiscoveryDebugAgent: Efficient and Interpretable Error Slice Discovery for Comprehensive Model Debugging
Despite the significant success of deep learning models in computer vision, they often exhibit systematic failures on specific data subsets, known as error slices. Identifying and mitigating these error slices is crucial…
image-classificationImage Classificationobject-detectionObject Detection+2LADDER: Language Driven Slice Discovery and Error Rectification
Error slice discovery is crucial to diagnose and mitigate model errors. Current clustering or discrete attribute-based slice discovery methods face key limitations: 1) clustering results in incoherent slices, while assig…
AttributeClusteringImage ClassificationLanguage Modelling+2Slicing Through Bias: Explaining Performance Gaps in Medical Image Analysis using Slice Discovery Methods
Machine learning models have achieved high overall accuracy in medical image analysis. However, performance disparities on specific patient groups pose challenges to their clinical utility, safety, and fairness. This can…
FairnessMedical Image AnalysisSlice DiscoveryLLM as Dataset Analyst: Subpopulation Structure Discovery with Large Language Model
The distribution of subpopulations is an important property hidden within a dataset. Uncovering and analyzing the subpopulation distribution within datasets provides a comprehensive understanding of the datasets, standin…
Image CaptioningInstruction FollowingLanguage ModelingLanguage Modelling+3Error Discovery by Clustering Influence Embeddings
We present a method for identifying groups of test examples -- slices -- on which a model under-performs, a task now known as slice discovery. We formalize coherence -- a requirement that erroneous predictions, within a …
ClusteringSlice DiscoveryVLSlice: Interactive Vision-and-Language Slice Discovery
Recent work in vision-and-language demonstrates that large-scale pretraining can learn generalizable models that are efficiently transferable to downstream tasks. While this may improve dataset-scale aggregate metrics, a…
Slice DiscoveryWhere Does My Model Underperform? A Human Evaluation of Slice Discovery Algorithms
Machine learning (ML) models that achieve high average accuracy can still underperform on semantically coherent subsets ("slices") of data. This behavior can have significant societal consequences for the safety or bias …
object-detectionObject DetectionSlice DiscoveryDomino: Discovering Systematic Errors with Cross-Modal Embeddings
Machine learning models that achieve high overall accuracy often make systematic errors on important subsets (or slices) of data. Identifying underperforming slices is particularly challenging when working with high-dime…
Representation LearningSlice DiscoveryTime Series Analysis