Knowledge catalogue
arXiv:2607.27904v1 Announce Type: new Abstract: Concept-based explanations are a prevalent way to explain the decisions of complex black-box methods through semantically meaningful, human-interpretabl
arXiv:2509.20899v3 Announce Type: replace Abstract: Concept Bottleneck Models (CBMs) enable interpretable image classification by structuring predictions around human-understandable concepts, but exte
arXiv:2604.16481v1 Announce Type: new Abstract: Large-scale text-to-image (T2I) diffusion models deliver remarkable visual fidelity but pose safety risks due to their capacity to reproduce undesirable
arXiv:2605.24856v1 Announce Type: cross Abstract: Concept formation in transformer language models is depth-extended, not a single-layer event: concepts emerge gradually across a contiguous region of
arXiv:2605.25574v1 Announce Type: cross Abstract: Concept erasure has emerged as a key research direction for ensuring safe and ethical image synthesis in Text-to-Image (T2I) models. While existing st
arXiv:2604.19323v1 Announce Type: cross Abstract: Concept Bottleneck Models (CBMs) route predictions exclusively through a clinically grounded concept layer, binding interpretability to concept-label
arXiv:2604.18868v1 Announce Type: new Abstract: The reasoning process of Graph Neural Networks is complex and considered opaque, limiting trust in their predictions. To alleviate this issue, prior wor
arXiv:2604.15748v1 Announce Type: new Abstract: Recently impressive performance has been achieved in Concept Bottleneck Models (CBM) by utilizing the image-text alignment learned by a large pre-traine
arXiv:2604.16076v1 Announce Type: cross Abstract: Concept Bottleneck Models (CBMs) aim to improve interpretability in Deep Learning by structuring predictions through human-understandable concepts, bu
arXiv:2512.07355v2 Announce Type: replace Abstract: Two traditions of interpretability have evolved side by side but seldom spoken to each other: Concept Bottleneck Models (CBMs), which prescribe what
arXiv:2606.08477v1 Announce Type: new Abstract: Knowledge extraction from symbolic data often produces abstractions that are formally defined but not immediately interpretable by users. Formal Concept
arXiv:2608.10004v1 Announce Type: new Abstract: Concept Bottleneck Models (CBMs) provide an interpretable framework by grounding predictions in human-understandable concepts, enabling semantic inspect
arXiv:2608.10985v1 Announce Type: new Abstract: Erasing concepts from large-scale text-to-image (T2I) diffusion models has become increasingly crucial due to the growing concerns over copyright infrin
arXiv:2601.06163v2 Announce Type: replace Abstract: The widespread adoption of text-to-image (T2I) diffusion models has raised concerns about their potential to generate copyrighted, inappropriate, or
arXiv:2605.16405v1 Announce Type: new Abstract: Concept-bottleneck models (CBMs) are neural classifiers that compute predictions from high-level concepts extracted from the input. CBMs ensure stakehol
arXiv:2601.14330v2 Announce Type: replace Abstract: Concept erasure aims to suppress sensitive content in diffusion models, but recent studies show that erased concepts can still be reawakened, reveal
arXiv:2506.18493v2 Announce Type: replace Abstract: Customizing image generation remains a core challenge in controllable image synthesis. For single-concept generation, maintaining both identity pres
arXiv:2604.24170v1 Announce Type: new Abstract: Concept Bottleneck Models (CBMs) predict through human-interpretable concepts, but they typically output point concept probabilities that conflate epist
arXiv:2512.12469v3 Announce Type: replace Abstract: We introduce Sparse Concept Anchoring, a method that biases latent space to position a targeted subset of concepts while allowing others to self-org
arXiv:2412.12242v2 Announce Type: replace-cross Abstract: Creative visual concept generation often draws inspiration from specific concepts in a reference image to produce relevant outcomes. However,
arXiv:2603.11795v2 Announce Type: replace Abstract: Unsupervised Concept Extraction aims to extract concepts from a single image; however, existing methods suffer from the inability to extract composa
arXiv:2502.08921v2 Announce Type: replace-cross Abstract: The task of text-to-image generation has achieved tremendous success in practice, with emerging concept generation models capable of producing
arXiv:2608.07208v1 Announce Type: cross Abstract: Existing measures of how much a text is about a concept read the surface of the text: dictionary word shares, topic proportions, embedding similaritie
arXiv:2604.20082v1 Announce Type: new Abstract: The trust in the predictions of Graph Neural Networks is limited by their opaque reasoning process. Prior methods have tried to explain graph networks v
arXiv:2606.05471v1 Announce Type: new Abstract: Learning semantics is essential for deep learning models to be interpretable and better aligned with human reasoning. Concept-based models approach this
arXiv:2508.14255v2 Announce Type: replace Abstract: Concept Bottleneck Models (CBMs) provide explicit interpretations for deep neural networks through concepts and allow intervention with concepts to
arXiv:2607.23492v1 Announce Type: new Abstract: Concept erasure techniques (CETs) edit text-to-image diffusion models to erase undesired targets such as NSFW content or copyrighted styles, while prese
arXiv:2607.24101v1 Announce Type: cross Abstract: Concept unlearning is increasingly used to limit the reproduction of protected or unsafe visual concepts in text-to-image models. Existing evaluations
arXiv:2606.26711v1 Announce Type: new Abstract: Transforming foundation segmentation models from human-prompted tools into auto-promptable annotators is critical for scalable medical data annotation.
arXiv:2606.26891v1 Announce Type: cross Abstract: Concept Bottleneck Models (CBMs) promise transparent reasoning by predicting through human-interpretable concepts, yet their effectiveness fundamental
arXiv:2507.07532v4 Announce Type: replace Abstract: While Prover-Verifier Games (PVGs) offer a promising path toward verifiability in nonlinear classification models, they have not yet been applied to
arXiv:2606.22567v1 Announce Type: new Abstract: Few-shot prompt learning is an effective strategy for adapting CLIP to downstream tasks, but class-only prompt optimization can overfit base-class super
arXiv:2606.21197v1 Announce Type: new Abstract: Vision Language Models (VLMs) have demonstrated impressive performance in tasks requiring joint understanding of images and text, such as image captioni
arXiv:2602.02726v2 Announce Type: replace-cross Abstract: Large language models (LLMs) encode rich semantic information in their hidden states, yet it remains difficult to understand what information
arXiv:2605.14309v1 Announce Type: cross Abstract: Machine unlearning in Vision-Language Models (VLMs) is typically performed at the image or instance level, making it difficult to precisely remove tar
arXiv:2602.17949v2 Announce Type: replace-cross Abstract: Background: Clinical named entity recognition tools commonly map free text to Unified Medical Language System (UMLS) Concept Unique Identifier
arXiv:2605.14210v1 Announce Type: cross Abstract: Concept Bottleneck Models (CBMs) offer interpretable alternatives to black-box predictors by introducing human-relatable concepts before the final out
arXiv:2510.27285v4 Announce Type: replace Abstract: Concept erasure methods aim to remove specific unsafe target concepts in diffusion models while preserving image generation utility. To address the
arXiv:2605.20908v1 Announce Type: new Abstract: Concept-based (CB) models provide interpretability and support test-time human intervention, while standard neural networks (NN) offer strong task perfo
arXiv:2605.20385v1 Announce Type: new Abstract: Recent progress in promptable segmentation has shifted visual perception from object-level localization toward concept-level understanding. However, the
arXiv:2605.20197v1 Announce Type: new Abstract: Medical concept extraction from electronic health records underpins many downstream applications, yet remains challenging because medically meaningful c
arXiv:2602.04093v2 Announce Type: replace Abstract: Concept-based Models (CMs) enhance interpretability in deep learning by grounding predictions in human-understandable concepts. However, concept ann
arXiv:2510.15770v3 Announce Type: replace Abstract: Concept Bottleneck Models (CBMs) enhance interpretability by predicting human-understandable concepts as intermediate representations. However, exis
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
arXiv:2512.05038v2 Announce Type: replace Abstract: Concept vectors aim to enhance model interpretability by linking internal representations with human-understandable semantics, but their practical u
arXiv:2604.21041v1 Announce Type: new Abstract: Machine unlearning for text-to-image diffusion models aims to selectively remove undesirable concepts from pre-trained models without costly retraining.
arXiv:2511.12968v2 Announce Type: replace Abstract: Concept erasure aims to remove harmful, inappropriate, or copyrighted content from text-to-image diffusion models while preserving non-target semant
arXiv:2605.01309v1 Announce Type: new Abstract: Long-tailed distributions are common in real-world recognition tasks, where a few head classes have many samples while most tail classes have very few.
arXiv:2605.01381v1 Announce Type: new Abstract: Understanding how neural models represent human-interpretable concepts is challenging. Prior work has explored linear concept subspaces from diverse per
arXiv:2604.24936v1 Announce Type: new Abstract: Techniques for concept extraction, such as sparse autoencoders and transcoders, aim to extract high-level symbolic concepts from low-level nonsymbolic r
arXiv:2607.20691v1 Announce Type: cross Abstract: Concept Bottleneck Models provide interpretable-by-design predictions by mediating diagnosis through human-understandable concepts, but in medical ima
arXiv:2607.17890v2 Announce Type: replace Abstract: Concept erasure aims to remove semantic concepts from a trained generative model and is increasingly important for responsible AI deployment. Howeve
arXiv:2607.03973v1 Announce Type: new Abstract: Concept erasure aims to remove a target concept from a representation while preserving the other information encoded in it. This is difficult because re
arXiv:2606.07007v1 Announce Type: cross Abstract: We propose a unified mathematical framework for a geometric understanding of concept learning and neuron interpretation in sparse autoencoders (SAEs).
arXiv:2606.04326v1 Announce Type: cross Abstract: Concept bottleneck models predict outcomes from high-level concepts detected in inputs. Although concepts provide a simple way to reap benefits from i
arXiv:2605.19855v1 Announce Type: cross Abstract: Concept-based Explainable Artificial Intelligence (XAI) interprets deep learning models using human-understandable visual features (e.g., textures or
arXiv:2603.07561v2 Announce Type: replace Abstract: Existing concept customization methods have achieved remarkable outcomes in high-fidelity and multi-concept customization. However, they often negle
arXiv:2605.28902v1 Announce Type: new Abstract: Concept erasure has emerged as a promising approach to mitigate undesired or unsafe content in diffusion models, yet existing methods still face signifi
arXiv:2605.22469v1 Announce Type: new Abstract: Evaluating single-concept personalization in text-to-image diffusion requires measuring both concept preservation, which captures identity fidelity to a
arXiv:2510.25512v2 Announce Type: replace-cross Abstract: Deep networks have shown remarkable performance across a wide range of tasks, yet getting a global concept-level understanding of how they fun