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- Evaluating the Robustness of Neural Networks: An Extreme Value. . .
Experimental results on various networks, including ResNet, Inception-v3 and MobileNet, show that (i) CLEVER is aligned with the robustness indication measured by the $\ell_2$ and $\ell_\infty$ norms of adversarial examples from powerful attacks, and (ii) defended networks using defensive distillation or bounded ReLU indeed give better CLEVER
- Counterfactual Debiasing for Fact Verification - OpenReview
016 namely CLEVER, which is augmentation-free 017 and mitigates biases on the inference stage 018 Specifically, we train a claim-evidence fusion 019 model and a claim-only model independently 020 Then, we obtain the final prediction via sub-021 tracting output of the claim-only model from 022 output of the claim-evidence fusion model,
- Leaving the barn door open for Clever Hans: Simple features predict. . .
This phenomenon, widely known in human and animal experiments, is often referred to as the ‘Clever Hans’ effect, where tasks are solved using spurious cues, often involving much simpler processes than those putatively assessed Previous research suggests that language models can exhibit this behaviour as well
- Weakly-Supervised Affordance Grounding Guided by Part-Level. . .
In this work, we focus on the task of weakly supervised affordance grounding, where a model is trained to identify affordance regions on objects using human-object interaction images and egocentric object images without dense labels
- Learnable Representative Coefficient Image Denoiser for. . .
Recently, HSI denoising models based on representative coefficient images (RCIs) under the spectral low-rank decomposition framework have garnered significant attention due to their clever utilization of spatial-spectral information in HSI at a low cost
- EVALUATING THE ROBUSTNESS OF NEURAL NET : A E VALUE THEORY APPROACH
CLEVER score is attack-agnostic and computationally feasible for large neural networks Experimental results on various networks, including ResNet, Inception-v3 and MobileNet, show that (i) CLEVER is aligned with the robustness indica-tion measured by the ‘ 2 and ‘ 1norms of adversarial examples from powerful
- Submissions - OpenReview
Leaving the barn door open for Clever Hans: Simple features predict LLM benchmark answers Lorenzo Pacchiardi , Marko Tesic , Lucy G Cheke , Jose Hernandez-Orallo 27 Sept 2024 (modified: 05 Feb 2025)
- LLaVA-OneVision: Easy Visual Task Transfer - OpenReview
We present LLaVA-OneVision, a family of open large multimodal models (LMMs) developed by consolidating our insights into data, models, and visual representations in the LLaVA-NeXT blog series
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