{"list":"other","total":53,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1409.1556","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","created":"2014-09-04","cited_by_count":15364},{"id":"1412.6572","title":"Explaining and Harnessing Adversarial Examples","created":"2014-12-20","cited_by_count":7364},{"id":"1706.06083","title":"Towards Deep Learning Models Resistant to Adversarial Attacks","created":"2017-06-19","cited_by_count":6415},{"id":"1505.07818","title":"Domain-Adversarial Training of Neural Networks","created":"2015-05-28","cited_by_count":3044},{"id":"1409.7495","title":"Unsupervised Domain Adaptation by Backpropagation","created":"2014-09-26","cited_by_count":1917},{"id":"1706.02690","title":"Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks","created":"2017-06-08","cited_by_count":1328},{"id":"1702.05464","title":"Adversarial Discriminative Domain Adaptation","created":"2017-02-17","cited_by_count":1311},{"id":"2010.03759","title":"Energy-based Out-of-distribution Detection","created":"2020-10-08","cited_by_count":1299},{"id":"2001.06782","title":"Gradient Surgery for Multi-Task Learning","created":"2020-01-19","cited_by_count":1256},{"id":"1712.05526","title":"Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning","created":"2017-12-15","cited_by_count":1055},{"id":"2306.01708","title":"TIES-Merging: Resolving Interference When Merging Models","created":"2023-06-02","cited_by_count":818},{"id":"1604.03539","title":"Cross-stitch Networks for Multi-task Learning","created":"2016-04-12","cited_by_count":467},{"id":"2110.14048","title":"Conflict-Averse Gradient Descent for Multi-task Learning","created":"2021-10-26","cited_by_count":459},{"id":"1811.03728","title":"Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering","created":"2018-11-09","cited_by_count":456},{"id":"2007.08745","title":"Backdoor Learning: A Survey","created":"2020-07-17","cited_by_count":426},{"id":"1912.08795","title":"Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion","created":"2019-12-18","cited_by_count":423},{"id":"2105.10056","title":"Data-Free Knowledge Distillation for Heterogeneous Federated Learning","created":"2021-05-20","cited_by_count":383},{"id":"1702.02284","title":"Adversarial Attacks on Neural Network Policies","created":"2017-02-08","cited_by_count":320},{"id":"2106.04619","title":"Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style","created":"2021-06-08","cited_by_count":312},{"id":"2205.09310","title":"Mitigating Neural Network Overconfidence with Logit Normalization","created":"2022-05-19","cited_by_count":252},{"id":"1902.11237","title":"A new Backdoor Attack in CNNs by training set corruption without label poisoning","created":"2019-02-12","cited_by_count":242},{"id":"2203.09249","title":"Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning","created":"2022-03-17","cited_by_count":172},{"id":"2104.11315","title":"SPECTRE: Defending Against Backdoor Attacks Using Robust Statistics","created":"2021-04-22","cited_by_count":130},{"id":"1905.09768","title":"Zero-shot Knowledge Transfer via Adversarial Belief Matching","created":"2019-05-23","cited_by_count":129},{"id":"2206.12654","title":"BackdoorBench: A Comprehensive Benchmark of Backdoor Learning","created":"2022-06-25","cited_by_count":128},{"id":"1908.00686","title":"Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection","created":"2019-08-02","cited_by_count":124},{"id":"2011.14779","title":"Data-Free Model Extraction","created":"2020-11-30","cited_by_count":124},{"id":"2005.03161","title":"MAZE: Data-Free Model Stealing Attack Using Zeroth-Order Gradient Estimation","created":"2020-05-06","cited_by_count":99},{"id":"2402.02705","title":"Representation Surgery for Multi-Task Model Merging","created":"2024-02-05","cited_by_count":96},{"id":"1912.11006","title":"Data-Free Adversarial Distillation","created":"2019-12-23","cited_by_count":90},{"id":"2005.04136","title":"Data-Free Network Quantization With Adversarial Knowledge Distillation","created":"2020-05-08","cited_by_count":80},{"id":"2106.06916","title":"Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Authorization","created":"2021-06-13","cited_by_count":52},{"id":"2206.08675","title":"Understanding Robust Overfitting of Adversarial Training and Beyond","created":"2022-06-17","cited_by_count":51},{"id":"1807.10108","title":"Effects of Degradations on Deep Neural Network Architectures","created":"2018-07-26","cited_by_count":48},{"id":"2207.03162","title":"Harnessing Out-Of-Distribution Examples via Augmenting Content and Style","created":"2022-07-07","cited_by_count":43},{"id":"2302.03015","title":"Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness","created":"2023-02-06","cited_by_count":36},{"id":"2012.05578","title":"Large-Scale Generative Data-Free Distillation","created":"2020-12-10","cited_by_count":29},{"id":"2303.13087","title":"Robust Generalization against Photon-Limited Corruptions via Worst-Case Sharpness Minimization","created":"2023-03-23","cited_by_count":29},{"id":"2303.06869","title":"Adaptive Data-Free Quantization","created":"2023-03-13","cited_by_count":26},{"id":"2205.14826","title":"Robust Weight Perturbation for Adversarial Training","created":"2022-05-30","cited_by_count":25},{"id":"2310.00258","title":"NAYER: Noisy Layer Data Generation for Efficient and Effective Data-free Knowledge Distillation","created":"2023-09-30","cited_by_count":22},{"id":"2102.03482","title":"Understanding the Interaction of Adversarial Training with Noisy Labels","created":"2021-02-06","cited_by_count":19},{"id":"2303.11078","title":"Model Barrier: A Compact Un-Transferable Isolation Domain for Model Intellectual Property Protection","created":"2023-03-20","cited_by_count":14},{"id":"2310.16391","title":"Winning Prize Comes from Losing Tickets: Improve Invariant Learning by Exploring Variant Parameters for Out-of-Distribution Generalization","created":"2023-10-25","cited_by_count":12},{"id":"2405.16560","title":"Task Groupings Regularization: Data-Free Meta-Learning with Heterogeneous Pre-trained Models","created":"2024-05-26","cited_by_count":12},{"id":"2306.02368","title":"Revisiting Data-Free Knowledge Distillation with Poisoned Teachers","created":"2023-06-04","cited_by_count":9},{"id":"2503.17198","title":"Jailbreaking the Non-Transferable Barrier via Test-Time Data Disguising","created":"2025-03-21","cited_by_count":8},{"id":"2408.13161","title":"Say No to Freeloader: Protecting Intellectual Property of Your Deep Model","created":"2024-08-23","cited_by_count":7},{"id":"2502.13593","title":"Toward Robust Non-Transferable Learning: A Survey and Benchmark","created":"2025-02-19","cited_by_count":7},{"id":"2210.12651","title":"Unsupervised Non-transferable Text Classification","created":"2022-10-23","cited_by_count":6},{"id":"2403.04149","title":"MAP: MAsk-Pruning for Source-Free Model Intellectual Property Protection","created":"2024-03-07","cited_by_count":4},{"id":"2410.08015","title":"Non-transferable Pruning","created":"2024-10-10","cited_by_count":2}],"next_cursor":null}