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How well does Classification Accuracy capture Concept Drift Detection Quality? An overview of Concept Drift Detection evaluation

arXiv:2605.31186v1 Announce Type: new Abstract: Data streams are nowadays among the most frequently analyzed data structures, with the concept drift posing a major challenge encountered by processing

researcharxiv-cs-lg

1 Jun 2026

Search: “concepts”

Contrastive Concept Importance: Explaining Pairwise Class Decisions Through Automatically Extracted Concept Representations

Concepts in Motion: Temporal Concept Bottleneck Model for Interpretable Video Classification

Erasing Thousands of Concepts: Towards Scalable and Practical Concept Erasure for Text-to-Image Diffusion Models

The Concept Allocation Zone: Tracking How Concepts Form Across Transformer Depth

Concept Inconsistency in Dermoscopic Concept Bottleneck Models: A Rough-Set Analysis of the Derm7pt Dataset

Concept-wise Attention for Fine-grained Concept Bottleneck Models

A Geometric Unification of Concept Learning with Concept Cones

ReCBM: Uncertainty-Gated Relational Reasoning for Concept Bottleneck Models

Forget-It-All: Multi-Concept Machine Unlearning via Concept-Aware Neuron Masking

ShowFlow: From Robust Single Concept to Condition-Free Multi-Concept Generation

OmniPrism: Learning Disentangled Visual Concept for Image Generation

Detecting Malicious Concepts without Image Generation in AI-Generated Content (AIGC)

Measuring Concept Content in Text from LLM Activations: ESG Evidence from Concept Vectors and Linear Probes

Concepts Worth Having: Refining VLM-Guided Concept Bottleneck Models with Minimal Annotations

Concept Graph Convolutions: Message Passing in the Concept Space

Prototype-Grounded Concept Models for Verifiable Concept Alignment

Credal Concept Bottleneck Models for Epistemic-Aleatoric Uncertainty Decomposition

Formal Concept Lattices are Good Semantic Scaffolds for Concept-Based Learning

Graph Concept Bottleneck Models

Subgraph Concept Networks: Concept Levels in Graph Classification

Intrinsic Concept Extraction Based on Compositional Interpretability

To Erase, or Not to Erase: Robust Training-Free Concept Erasure with Preservation aware Adaptive Ranked Subspace Expansion

Mask to Concept: Auto-Promptable SAM3 via Efficient Test-Time Concept Embedding Search for Few-Shot Annotation

Neural Concept Verifier: Scaling Prover-Verifier Games via Concept Encodings

Sparse Concept Anchoring for Interpretable and Controllable Neural Representations

Vector Quantized Latent Concepts: A Scalable Alternative to Clustering-Based Concept Discovery

ICED: Concept-level Machine Unlearning via Interpretable Concept Decomposition

PEAK: Precise and Persistent Concept Erasure via k-Sparse Autoencoders

Rethinking Robust Adversarial Concept Erasure in Diffusion Models

SynCB: A Synergy Concept-Based Model with Dynamic Routing Between Concepts and Complementary Neural Branches

Federated Concept-Based Models: Interpretable models with distributed supervision

Mitigating Spurious Background Bias in Multimedia Recognition with Disentangled Concept Bottlenecks

Concept-Constrained Prompt Learning for Few-Shot CLIP Adaptation

How well does Classification Accuracy capture Concept Drift Detection Quality? An overview of Concept Drift Detection evaluation

ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning

CUICurate: A GraphRAG-based Framework for Automated Clinical Concept Curation for NLP applications

Projected Gradient Unlearning for Text-to-Image Diffusion Models: Defending Against Concept Revival Attacks

GrOCE:Graph-Guided Online Concept Erasure for Text-to-Image Diffusion Models

LU-500: A Logo Benchmark for Concept Unlearning

The SuperActivator Mechanism: Transformers Concentrate Reliable Concept Signals in the Tail

Mosaic: Compositional Multi-Concept Erasure via Vector Field Blending

MedicalBench: Evaluating Large Language Models Toward Improved Medical Concept Extraction

Towards Fine-Grained and Verifiable Concept Bottleneck Models

CUE: Concept-Aware Multi-Label Expansion to Mitigate Concept Confusion in Long-Tailed Learning

A Unifying Framework for Unsupervised Concept Extraction

Spatially Grounded Concept Bottleneck Models for Trustworthy Breast Ultrasound Diagnosis

Stress Testing Concept Erasure with Large Language Model Agents

MANCE: Manifold Aware Concept Erasure