{"total":87,"rows":[{"id":"2105.07581","title":"Vision Transformers are Robust Learners","created":"2021-05-17","cited_by_count":207,"area":null},{"id":"2106.07411","title":"Partial success in closing the gap between human and machine vision","created":"2021-06-14","cited_by_count":185,"area":null},{"id":"2203.01441","title":"3D Common Corruptions and Data Augmentation","created":"2022-03-02","cited_by_count":119,"area":null},{"id":"2106.06047","title":"Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning","created":"2021-06-10","cited_by_count":111,"area":null},{"id":"2103.06922","title":"Towards Interpreting and Mitigating Shortcut Learning Behavior of NLU Models","created":"2021-03-11","cited_by_count":78,"area":null},{"id":"2310.16228","title":"On the Foundations of Shortcut Learning","created":"2023-10-24","cited_by_count":65,"area":null},{"id":"2205.08303","title":"MulT: An End-to-End Multitask Learning Transformer","created":"2022-05-17","cited_by_count":48,"area":null},{"id":"2310.17626","title":"A Survey on Transferability of Adversarial Examples across Deep Neural Networks","created":"2023-10-26","cited_by_count":44,"area":null},{"id":"2110.14189","title":"Robust Contrastive Learning Using Negative Samples with Diminished Semantics","created":"2021-10-27","cited_by_count":43,"area":null},{"id":"2110.02424","title":"Spectral Bias in Practice: The Role of Function Frequency in Generalization","created":"2021-10-06","cited_by_count":37,"area":null},{"id":"2106.02105","title":"A Little Robustness Goes a Long Way: Leveraging Robust Features for Targeted Transfer Attacks","created":"2021-06-03","cited_by_count":34,"area":null},{"id":"2104.09789","title":"Does enhanced shape bias improve neural network robustness to common corruptions?","created":"2021-04-20","cited_by_count":32,"area":null},{"id":"2307.12280","title":"Downstream-agnostic Adversarial Examples","created":"2023-07-23","cited_by_count":32,"area":null},{"id":"2106.15231","title":"Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis","created":"2021-06-29","cited_by_count":30,"area":null},{"id":"2307.16851","title":"Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectives","created":"2023-07-31","cited_by_count":30,"area":"alignment"},{"id":"2308.12553","title":"Don't blame Dataset Shift! Shortcut Learning due to Gradients and Cross Entropy","created":"2023-08-24","cited_by_count":30,"area":null},{"id":"2110.10232","title":"Test time Adaptation through Perturbation Robustness","created":"2021-10-19","cited_by_count":28,"area":null},{"id":"2212.07101","title":"Domain Generalization by Learning and Removing Domain-specific Features","created":"2022-12-14","cited_by_count":28,"area":null},{"id":"2307.09829","title":"What do neural networks learn in image classification? A frequency shortcut perspective","created":"2023-07-19","cited_by_count":28,"area":null},{"id":"2110.06399","title":"Dynamic Inference with Neural Interpreters","created":"2021-10-12","cited_by_count":24,"area":null},{"id":"2103.10919","title":"Robustness via Cross-Domain Ensembles","created":"2021-03-19","cited_by_count":23,"area":null},{"id":"2112.14337","title":"Closer Look at the Transferability of Adversarial Examples: How They Fool Different Models Differently","created":"2021-12-29","cited_by_count":22,"area":null},{"id":"2206.05263","title":"Causal Balancing for Domain Generalization","created":"2022-06-10","cited_by_count":20,"area":null},{"id":"2101.11453","title":"Meta Adversarial Training against Universal Patches","created":"2021-01-27","cited_by_count":19,"area":null},{"id":"2102.06479","title":"Universal Adversarial Perturbations Through the Lens of Deep Steganography: Towards A Fourier Perspective","created":"2021-02-12","cited_by_count":18,"area":null},{"id":"2203.06498","title":"The worst of both worlds: A comparative analysis of errors in learning from data in psychology and machine learning","created":"2022-03-12","cited_by_count":17,"area":null},{"id":"2103.02451","title":"Multimodal Scale Consistency and Awareness for Monocular Self-Supervised Depth Estimation","created":"2021-03-03","cited_by_count":16,"area":null},{"id":"2412.01876","title":"Understanding Bias in Large-Scale Visual Datasets","created":"2024-12-02","cited_by_count":15,"area":null},{"id":"2111.03740","title":"Toward Learning Human-aligned Cross-domain Robust Models by Countering Misaligned Features","created":"2021-11-05","cited_by_count":14,"area":null},{"id":"2212.06641","title":"Simplicity Bias Leads to Amplified Performance Disparities","created":"2022-12-13","cited_by_count":11,"area":null},{"id":"2103.03048","title":"Detecting Spurious Correlations with Sanity Tests for Artificial Intelligence Guided Radiology Systems","created":"2021-03-04","cited_by_count":10,"area":null},{"id":"2108.13696","title":"Phy-Q as a measure for physical reasoning intelligence","created":"2021-08-31","cited_by_count":10,"area":null},{"id":"2202.07184","title":"On the Origins of the Block Structure Phenomenon in Neural Network Representations","created":"2022-02-15","cited_by_count":10,"area":null},{"id":"2309.15762","title":"Rapid Network Adaptation: Learning to Adapt Neural Networks Using Test-Time Feedback","created":"2023-09-27","cited_by_count":10,"area":null},{"id":"2203.04607","title":"Practical No-box Adversarial Attacks with Training-free Hybrid Image Transformation","created":"2022-03-09","cited_by_count":9,"area":null},{"id":"2103.12719","title":"Characterizing and Improving the Robustness of Self-Supervised Learning through Background Augmentations","created":"2021-03-23","cited_by_count":8,"area":null},{"id":"2104.12679","title":"Impact of Spatial Frequency Based Constraints on Adversarial Robustness","created":"2021-04-26","cited_by_count":8,"area":null},{"id":"2205.03154","title":"Investigating and Explaining the Frequency Bias in Image Classification","created":"2022-05-06","cited_by_count":8,"area":null},{"id":"2208.09602","title":"Exploring Adversarial Robustness of Vision Transformers in the Spectral Perspective","created":"2022-08-20","cited_by_count":8,"area":null},{"id":"2102.05110","title":"Adversarial Perturbations Are Not So Weird: Entanglement of Robust and Non-Robust Features in Neural Network Classifiers","created":"2021-02-09","cited_by_count":7,"area":null},{"id":"2106.06654","title":"Disrupting Model Training with Adversarial Shortcuts","created":"2021-06-12","cited_by_count":7,"area":null},{"id":"2111.04204","title":"Natural Adversarial Objects","created":"2021-11-07","cited_by_count":7,"area":null},{"id":"2202.12412","title":"Fourier-Based Augmentations for Improved Robustness and Uncertainty Calibration","created":"2022-02-24","cited_by_count":7,"area":null},{"id":"2303.02034","title":"Linear CNNs Discover the Statistical Structure of the Dataset Using Only the Most Dominant Frequencies","created":"2023-03-03","cited_by_count":7,"area":null},{"id":"2311.15438","title":"ProtoArgNet: Interpretable Image Classification with Super-Prototypes and Argumentation [Technical Report]","created":"2023-11-26","cited_by_count":7,"area":null},{"id":"2108.12510","title":"Pulling Up by the Causal Bootstraps: Causal Data Augmentation for Pre-training Debiasing","created":"2021-08-27","cited_by_count":6,"area":null},{"id":"2503.17226","title":"DDB: Diffusion Driven Balancing to Address Spurious Correlations","created":"2025-03-21","cited_by_count":6,"area":"diffusion"},{"id":"2106.03831","title":"Counterfactual Maximum Likelihood Estimation for Training Deep Networks","created":"2021-06-07","cited_by_count":5,"area":null},{"id":"2206.05846","title":"InBiaseD: Inductive Bias Distillation to Improve Generalization and Robustness through Shape-awareness","created":"2022-06-12","cited_by_count":5,"area":null},{"id":"2303.14173","title":"How many dimensions are required to find an adversarial example?","created":"2023-03-24","cited_by_count":5,"area":null},{"id":"2305.14979","title":"Assessment of the Reliablity of a Model's Decision by Generalizing Attribution to the Wavelet Domain","created":"2023-05-24","cited_by_count":5,"area":null},{"id":"2412.08127","title":"Evil twins are not that evil: Qualitative insights into machine-generated prompts","created":"2024-12-11","cited_by_count":5,"area":null},{"id":"2202.04136","title":"Generative multitask learning mitigates target-causing confounding","created":"2022-02-08","cited_by_count":4,"area":null},{"id":"2210.13356","title":"Robust Self-Supervised Learning with Lie Groups","created":"2022-10-24","cited_by_count":4,"area":null},{"id":"2304.06672","title":"LSFSL: Leveraging Shape Information in Few-shot Learning","created":"2023-04-13","cited_by_count":4,"area":null},{"id":"2305.17041","title":"RFiD: Towards Rational Fusion-in-Decoder for Open-Domain Question Answering","created":"2023-05-26","cited_by_count":4,"area":null},{"id":"2311.06224","title":"Harnessing Synthetic Datasets: The Role of Shape Bias in Deep Neural Network Generalization","created":"2023-11-10","cited_by_count":4,"area":null},{"id":"2103.09916","title":"Can Targeted Adversarial Examples Transfer When the Source and Target Models Have No Label Space Overlap?","created":"2021-03-17","cited_by_count":3,"area":null},{"id":"2209.05104","title":"Bias Challenges in Counterfactual Data Augmentation","created":"2022-09-12","cited_by_count":3,"area":null},{"id":"2404.08069","title":"Persistent Classification: A New Approach to Stability of Data and Adversarial Examples","created":"2024-04-11","cited_by_count":3,"area":null},{"id":"2105.12427","title":"Deep Repulsive Prototypes for Adversarial Robustness","created":"2021-05-26","cited_by_count":2,"area":null},{"id":"2110.01670","title":"A manifold learning approach for gesture recognition from micro-Doppler radar measurements","created":"2021-10-04","cited_by_count":2,"area":null},{"id":"2208.00539","title":"Is current research on adversarial robustness addressing the right problem?","created":"2022-07-31","cited_by_count":2,"area":null},{"id":"2210.15565","title":"Bridging the visual gap in VLN via semantically richer instructions","created":"2022-10-27","cited_by_count":2,"area":null},{"id":"2306.13971","title":"Towards Robust Aspect-based Sentiment Analysis through Non-counterfactual Augmentations","created":"2023-06-24","cited_by_count":2,"area":null},{"id":"2309.12214","title":"Can We Reliably Improve the Robustness to Image Acquisition of Remote Sensing of PV Systems?","created":"2023-09-21","cited_by_count":2,"area":null},{"id":"2310.04519","title":"SPADE: Sparsity-Guided Debugging for Deep Neural Networks","created":"2023-10-06","cited_by_count":2,"area":null},{"id":"2606.12334","title":"Fourier Features Let Agents Learn High Precision Policies with Imitation Learning","created":"2026-06-10","cited_by_count":2,"area":"lr"},{"id":"2206.00893","title":"Leveraging Systematic Knowledge of 2D Transformations","created":"2022-06-02","cited_by_count":1,"area":null},{"id":"2208.07464","title":"An Overview and Prospective Outlook on Robust Training and Certification of Machine Learning Models","created":"2022-08-15","cited_by_count":1,"area":null},{"id":"2212.06021","title":"A novel feature-scrambling approach reveals the capacity of convolutional neural networks to learn spatial relations","created":"2022-12-12","cited_by_count":1,"area":null},{"id":"2301.13514","title":"Fourier Sensitivity and Regularization of Computer Vision Models","created":"2023-01-31","cited_by_count":1,"area":null},{"id":"2406.11517","title":"Revisiting Spurious Correlation in Domain Generalization","created":"2024-06-17","cited_by_count":1,"area":null},{"id":"2503.17110","title":"Beyond Accuracy: What Matters in Designing Well-Behaved Image Classification Models?","created":"2025-03-21","cited_by_count":1,"area":null},{"id":"2603.29313","title":"HSFM: Hard-Set-Guided Feature-Space Meta-Learning for Robust Classification under Spurious Correlations","created":"2026-03-31","cited_by_count":1,"area":null},{"id":"2105.07237","title":"Brain Inspired Face Recognition: A Computational Framework","created":"2021-05-15","cited_by_count":0,"area":null},{"id":"2106.04178","title":"White Paper Assistance: A Step Forward Beyond the Shortcut Learning","created":"2021-06-08","cited_by_count":0,"area":null},{"id":"2202.10668","title":"Automatically Generating Counterfactuals for Relation Classification","created":"2022-02-22","cited_by_count":0,"area":null},{"id":"2208.00766","title":"Tutorial on the development of AI models for medical image analysis","created":"2022-07-14","cited_by_count":0,"area":null},{"id":"2409.18951","title":"Spectral Wavelet Dropout: Regularization in the Wavelet Domain","created":"2024-09-27","cited_by_count":0,"area":null},{"id":"2503.05911","title":"Generalizable Image Repair for Robust Visual Control","created":"2025-03-07","cited_by_count":0,"area":null},{"id":"2506.18481","title":"FREQuency ATTribution: benchmarking frequency-based occlusion for time series data","created":"2025-06-23","cited_by_count":0,"area":null},{"id":"2605.20728","title":"Early High-Frequency Injection for Geometry-Sensitive OOD Detection","created":"2026-05-20","cited_by_count":0,"area":null},{"id":"2606.03493","title":"Low-Frequency Shortcuts in Texture-Driven Visual Learning","created":"2026-06-02","cited_by_count":0,"area":null},{"id":"2606.29416","title":"Can Machines Really See Objects in Images? A Study Based on Syntactic Distance and Visual Self-Referential Instances","created":"2026-06-28","cited_by_count":0,"area":null},{"id":"2607.16298","title":"Revisiting Shape and Texture Reliance with Category-Separability-Calibrated Suppression","created":"2026-07-13","cited_by_count":0,"area":null},{"id":"2608.19936","title":"Towards Quantifying Benchmark Optimization in ASR Models","created":"2026-08-20","cited_by_count":0,"area":null}],"next_cursor":null}