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版本更新 · v2arXiv:2605.31304

DUNE: Disentangling Polysemantic Neurons Without Losing Predictive Performance

Doğukan Bağcı, Bernt Schiele, Simone Schaub-Meyer, Jonas Fischer, Robin Hesse

原文摘要

Deep neural networks (DNNs) are widely used, but understanding what they actually learn remains difficult. A major obstacle is that individual neurons often respond to multiple unrelated concepts, obscuring the network's internal decision process. Existing approaches, such as sparse autoencoders, can disentangle these mixed signals into more meaningful, ``monosemantic'' features, but typically introduce approximation errors that may degrade downstream performance. Consequently, these methods exhibit a Pareto frontier between monosemanticity and functional faithfulness. To address this, we introduce DUNE (disentangling units with no error), a method that decomposes DNN neurons into more monosemantic subunits while preserving their original function by design. DUNE identifies concept-specific contributions from the preceding layer and re-routes them into separate subunits whose aggregation exactly recovers the original activation. DUNE can be applied directly to pretrained models, without labels or training of an auxiliary model. Across several models, including DINOv2, DUNE achieves state-of-the-art or competitive monosemanticity while exactly preserving functional faithfulness, substantially advancing the existing Pareto frontier between these properties. DUNE operates efficiently and enables fine-grained interventions such as concept-specific steering.

cs.LG · cs.CV

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2610.33857

TrackTok: Semantically Persistent Tokens for Latent Video Generation

Nhi Pham, Christopher Wewer, Bernt Schiele, Jonas Fischer, Jan Eric Lenssen

原文摘要

We introduce TrackTok, an efficient video tokenization framework that produces object-aligned tokens, i.e., tokens that consistently encode the same semantic object across time, even as it moves, deforms, or undergoes partial occlusion. Conventional patch-based or existing holistic tokenizers produce token identities often tied to fixed image locations. In contrast, our approach learns temporally persistent object-aligned representations that bind visual features across frames into stable semantic units. This enables substantially more compact, continuous latent video representations, reducing the number of tokens required for downstream generative modeling with state-of-the-art flow models, while preserving the semantic structure needed for high-quality synthesis. Our tokenizer offers a favorable quality-efficiency trade-off. TrackTok achieves comparable class-conditional generative FVD on UCF-101 dataset at 8 times fewer latent tokens and better latency, and outperforms existing 1D tokenization baselines on frame prediction task on Kinetics-600 across low token budgets. These results suggest that object-aligned and temporally persistent tokenization is a promising direction for scalable video generation, where reducing latent token counts is critical for efficient modeling. Our code for this work is available at github.com/phamleyennhi/tracktok.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2602.22968

Certified Circuits: Stability Guarantees for Mechanistic Circuits

Alaa Anani, Tobias Lorenz, Bernt Schiele, Mario Fritz, Jonas Fischer

原文摘要

Understanding how neural networks arrive at their predictions is essential for debugging, auditing, and deployment. Mechanistic interpretability pursues this goal by identifying circuits--minimal subnetworks responsible for specific behaviors. However, existing circuit discovery methods are brittle: circuits depend strongly on the chosen concept dataset and often fail to transfer out-of-distribution, raising doubts whether they capture the concept or merely dataset-specific artifacts. We introduce Certified Circuits, which provide provable stability guarantees for circuit discovery. Our framework wraps any black-box discovery algorithm with randomized data subsampling to certify that inclusion decisions over circuit components--neurons or edges of the model graph, depending on the base algorithm--are invariant to bounded edit-distance perturbations of the concept dataset. Unstable components are abstained from, yielding circuits that are more compact and more accurate. We validate across three architectures (ResNet, ViT, GPT-2) on vision (ImageNet and four OOD datasets) and language (IOI, IOI-Hard, Greater-Than) tasks. Certified circuits achieve up to 56% higher accuracy and up to 80% fewer components, and remain reliable where baselines degrade. Certified Circuits puts circuit discovery on formal ground by producing mechanistic explanations that are provably stable and better aligned with the target concept. Code: https://github.com/AlaaAnani/certified-circuits.

cs.AI · cs.CV · cs.CY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03438

Align Once to Explain: Feature Alignment for Scalable B-cosification of Foundational Vision Transformers

Raphael Maser, Siddhartha Gairola, Sukrut Rao, Bernt Schiele

原文摘要

Foundational vision models have become the de facto standard for many vision tasks due to their strong performance. However, they are notoriously opaque and remain hard to interpret. We present ALOE (ALign Once to Explain), a one-time, label-free feature alignment approach that efficiently converts foundational vision models into inherently interpretable B-cos variants. Once aligned, the B-cos backbone is used as a drop-in replacement across several downstream tasks-amortizing the cost of interpretability. ALOE is robust across pre-training paradigms (supervised, self-supervised, vision-language) and can preserve teacher-level generalization with orders of magnitude less data than training from scratch. On classification, it strongly outperforms vanilla B-cosification (e.g., +10.5 p.p. top-1 on ImageNet for DINOv3 ViT-B/16), retains strong linear probing, k-NN, and zero-shot transfer competitive with foundational backbones (DINOv3, SigLIP2) across diverse downstream datasets. It also preserves spatially structured features useful for dense prediction, while yielding well-localized and highly human-interpretable explanations by design.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38926

OneScene-Bench: Do Vision-Language Models Integrate Evidence Across Views?

Swathi Suhas, Anurag Das, Bernt Schiele, Jonas Fischer

原文摘要

Vision-language models (VLMs) increasingly process multiple views of the same scene, but it remains unclear whether they integrate these observations into a coherent scene-level representation. Existing multi-image evaluations do not isolate this capability, as tasks may admit correct solutions through independent per-view processing without requiring cross-view correspondence. We introduce OneScene-Bench, a controlled benchmark designed to explicitly distinguish when cross-view integration is necessary. Our benchmark evaluates three complementary capabilities on real-world multi-view scenes: unique object counting, cross-view consistency, and spatial reasoning. For counting, we control object overlap across views to separate cases solvable by simple per-view aggregation from those requiring instance-level correspondence. For consistency, we introduce localized edits in shared and view-specific regions, testing whether models distinguish genuine scene contradictions from differences caused by viewpoint and visibility. For spatial reasoning, we construct object relations that require combining spatial evidence across viewpoints to compare physical proximity. We evaluate twelve state-of-the-art open-weight and proprietary VLMs and find that counting accuracy drops substantially on complementary views, ranging from 4.5-50.0%, compared with 45.5-81.5% on complete-overlap examples. Moreover, cross-view consistency remains around 50% across models, including recent proprietary VLMs, despite substantially stronger performance on spatial reasoning. Our results reveal a substantial gap between processing multiple images and integrating evidence across them, highlighting the need for evaluations that explicitly test scene-level multi-view understanding. Code and data are available at https://github.com/swathisuhas/OneScene-Bench.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:1812.00740Berkeley BAIR

解耦对抗鲁棒性与泛化性

Disentangling Adversarial Robustness and Generalization

David Stutz, Matthias Hein, Bernt Schiele

AI 速读 基于摘要

论文通过假设低维数据流形，论证对抗鲁棒性与泛化性并非必然冲突，并区分流形外与流形上对抗样本。

cs.CV · cs.CR · cs.LG · stat.ML

arXiv ↗PDF ↗
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Obtaining deep networks that are robust against adversarial examples and generalize well is an open problem. A recent hypothesis even states that both robust and accurate models are impossible, i.e., adversarial robustness and generalization are conflicting goals. In an effort to clarify the relationship between robustness and generalization, we assume an underlying, low-dimensional data manifold and show that: 1. regular adversarial examples leave the manifold; 2. adversarial examples constrained to the manifold, i.e., on-manifold adversarial examples, exist; 3. on-manifold adversarial examples are generalization errors, and on-manifold adversarial training boosts generalization; 4. regular robustness and generalization are not necessarily contradicting goals. These assumptions imply that both robust and accurate models are possible. However, different models (architectures, training strategies etc.) can exhibit different robustness and generalization characteristics. To confirm our claims, we present extensive experiments on synthetic data (with known manifold) as well as on EMNIST, Fashion-MNIST and CelebA.