{"total":199,"rows":[{"id":"2203.05482","title":"Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time","created":"2022-03-10","cited_by_count":1396,"area":null},{"id":"2109.01903","title":"Robust fine-tuning of zero-shot models","created":"2021-09-04","cited_by_count":797,"area":null},{"id":"2202.10054","title":"Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution","created":"2022-02-21","cited_by_count":739,"area":null},{"id":"2210.11466","title":"Surgical Fine-Tuning Improves Adaptation to Distribution Shifts","created":"2022-10-20","cited_by_count":205,"area":null},{"id":"2102.11005","title":"LogME: Practical Assessment of Pre-trained Models for Transfer Learning","created":"2021-02-22","cited_by_count":172,"area":null},{"id":"2212.00638","title":"Finetune like you pretrain: Improved finetuning of zero-shot vision models","created":"2022-12-01","cited_by_count":150,"area":null},{"id":"2211.15583","title":"On the Effectiveness of Parameter-Efficient Fine-Tuning","created":"2022-11-28","cited_by_count":141,"area":null},{"id":"2203.10789","title":"Domain Generalization by Mutual-Information Regularization with Pre-trained Models","created":"2022-03-21","cited_by_count":133,"area":null},{"id":"2308.13320","title":"Fine-tuning can cripple your foundation model; preserving features may be the solution","created":"2023-08-25","cited_by_count":96,"area":null},{"id":"2210.17409","title":"Deep Model Reassembly","created":"2022-10-24","cited_by_count":78,"area":null},{"id":"2106.03765","title":"On Inductive Biases for Heterogeneous Treatment Effect Estimation","created":"2021-06-07","cited_by_count":75,"area":null},{"id":"2305.08252","title":"Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity","created":"2023-05-14","cited_by_count":56,"area":null},{"id":"2207.14381","title":"Pro-tuning: Unified Prompt Tuning for Vision Tasks","created":"2022-07-28","cited_by_count":53,"area":null},{"id":"2102.06605","title":"Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning","created":"2021-02-12","cited_by_count":50,"area":null},{"id":"2111.09613","title":"Improving Transferability of Representations via Augmentation-Aware Self-Supervision","created":"2021-11-18","cited_by_count":49,"area":null},{"id":"2111.04578","title":"Improved Regularization and Robustness for Fine-tuning in Neural Networks","created":"2021-11-08","cited_by_count":45,"area":null},{"id":"2301.01299","title":"Recent Advances on Federated Learning: A Systematic Survey","created":"2023-01-03","cited_by_count":41,"area":null},{"id":"2507.03971","title":"Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data","created":"2025-07-05","cited_by_count":41,"area":null},{"id":"2102.12903","title":"Self-Tuning for Data-Efficient Deep Learning","created":"2021-02-25","cited_by_count":39,"area":null},{"id":"2103.02193","title":"Adaptive Consistency Regularization for Semi-Supervised Transfer Learning","created":"2021-03-03","cited_by_count":37,"area":null},{"id":"2303.10720","title":"Trainable Projected Gradient Method for Robust Fine-tuning","created":"2023-03-19","cited_by_count":37,"area":null},{"id":"2111.12292","title":"Improved Fine-Tuning by Better Leveraging Pre-Training Data","created":"2021-11-24","cited_by_count":36,"area":null},{"id":"2106.15434","title":"Zoo-Tuning: Adaptive Transfer from a Zoo of Models","created":"2021-06-29","cited_by_count":35,"area":null},{"id":"2110.06014","title":"Rethinking Supervised Pre-training for Better Downstream Transferring","created":"2021-10-12","cited_by_count":35,"area":null},{"id":"2206.02659","title":"Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization Guarantees","created":"2022-06-06","cited_by_count":34,"area":null},{"id":"2107.04835","title":"Noise Stability Regularization for Improving BERT Fine-tuning","created":"2021-07-10","cited_by_count":29,"area":null},{"id":"2110.10545","title":"Ranking and Tuning Pre-trained Models: A New Paradigm for Exploiting Model Hubs","created":"2021-10-20","cited_by_count":28,"area":null},{"id":"2103.10415","title":"Refining Language Models with Compositional Explanations","created":"2021-03-18","cited_by_count":27,"area":null},{"id":"2103.01542","title":"TransTailor: Pruning the Pre-trained Model for Improved Transfer Learning","created":"2021-03-02","cited_by_count":25,"area":null},{"id":"2102.02805","title":"How do Quadratic Regularizers Prevent Catastrophic Forgetting: The Role of Interpolation","created":"2021-02-04","cited_by_count":19,"area":null},{"id":"2203.10453","title":"Fine-Tuning Graph Neural Networks via Graph Topology induced Optimal Transport","created":"2022-03-20","cited_by_count":19,"area":null},{"id":"2304.07574","title":"Exploring Incompatible Knowledge Transfer in Few-shot Image Generation","created":"2023-04-15","cited_by_count":19,"area":null},{"id":"2310.19182","title":"Fast Trainable Projection for Robust Fine-Tuning","created":"2023-10-29","cited_by_count":19,"area":null},{"id":"2402.11441","title":"InfuserKI: Enhancing Large Language Models with Knowledge Graphs via Infuser-Guided Knowledge Integration","created":"2024-02-18","cited_by_count":19,"area":null},{"id":"2304.01973","title":"ERM++: An Improved Baseline for Domain Generalization","created":"2023-04-04","cited_by_count":18,"area":null},{"id":"2306.03828","title":"Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How","created":"2023-06-06","cited_by_count":18,"area":null},{"id":"2203.10761","title":"Harnessing Hard Mixed Samples with Decoupled Regularizer","created":"2022-03-21","cited_by_count":17,"area":null},{"id":"2205.15234","title":"Few-Shot Adaptation of Pre-Trained Networks for Domain Shift","created":"2022-05-30","cited_by_count":16,"area":null},{"id":"2406.10050","title":"Comparison of fine-tuning strategies for transfer learning in medical image classification","created":"2024-06-14","cited_by_count":14,"area":null},{"id":"2407.15085","title":"Learn to Preserve and Diversify: Parameter-Efficient Group with Orthogonal Regularization for Domain Generalization","created":"2024-07-21","cited_by_count":14,"area":null},{"id":"2407.20999","title":"MoFO: Momentum-Filtered Optimizer for Mitigating Forgetting in LLM Fine-Tuning","created":"2024-07-30","cited_by_count":14,"area":null},{"id":"2409.17137","title":"PACE: Marrying generalization in PArameter-efficient fine-tuning with Consistency rEgularization","created":"2024-09-25","cited_by_count":14,"area":null},{"id":"2205.09904","title":"Deep transfer learning for image classification: a survey","created":"2022-05-20","cited_by_count":13,"area":null},{"id":"2205.10972","title":"Global Extreme Heat Forecasting Using Neural Weather Models","created":"2022-05-23","cited_by_count":13,"area":null},{"id":"2308.06960","title":"Search to Fine-tune Pre-trained Graph Neural Networks for Graph-level Tasks","created":"2023-08-14","cited_by_count":13,"area":null},{"id":"2310.02000","title":"MUSCLE: Multi-task Self-supervised Continual Learning to Pre-train Deep Models for X-ray Images of Multiple Body Parts","created":"2023-10-03","cited_by_count":13,"area":null},{"id":"2312.13583","title":"Fine-tuning Graph Neural Networks by Preserving Graph Generative Patterns","created":"2023-12-21","cited_by_count":13,"area":null},{"id":"2206.05658","title":"Improving Pre-trained Language Model Fine-tuning with Noise Stability Regularization","created":"2022-06-12","cited_by_count":12,"area":null},{"id":"2310.15105","title":"FD-Align: Feature Discrimination Alignment for Fine-tuning Pre-Trained Models in Few-Shot Learning","created":"2023-10-23","cited_by_count":12,"area":null},{"id":"2405.20271","title":"ETHER: Efficient Finetuning of Large-Scale Models with Hyperplane Reflections","created":"2024-05-30","cited_by_count":12,"area":null},{"id":"2410.07018","title":"Tri-Level Navigator: LLM-Empowered Tri-Level Learning for Time Series OOD Generalization","created":"2024-10-09","cited_by_count":12,"area":null},{"id":"2307.11958","title":"Pick the Best Pre-trained Model: Towards Transferability Estimation for Medical Image Segmentation","created":"2023-07-22","cited_by_count":11,"area":null},{"id":"2502.15895","title":"Directional Gradient Projection for Robust Fine-Tuning of Foundation Models","created":"2025-02-21","cited_by_count":11,"area":null},{"id":"2208.00238","title":"Improving Fine-tuning of Self-supervised Models with Contrastive Initialization","created":"2022-07-30","cited_by_count":10,"area":null},{"id":"2107.02378","title":"Learning an Explicit Hyperparameter Prediction Function Conditioned on Tasks","created":"2021-07-06","cited_by_count":9,"area":null},{"id":"2110.12183","title":"Attend and Guide (AG-Net): A Keypoints-driven Attention-based Deep Network for Image Recognition","created":"2021-10-23","cited_by_count":9,"area":null},{"id":"2402.12220","title":"Bayesian Parameter-Efficient Fine-Tuning for Overcoming Catastrophic Forgetting","created":"2024-02-19","cited_by_count":9,"area":null},{"id":"2404.02649","title":"On the Importance of Uncertainty in Decision-Making with Large Language Models","created":"2024-04-03","cited_by_count":9,"area":null},{"id":"2407.17877","title":"Advancing 3D Point Cloud Understanding through Deep Transfer Learning: A Comprehensive Survey","created":"2024-07-25","cited_by_count":9,"area":null},{"id":"2411.01713","title":"Rethinking Weight Decay for Robust Fine-Tuning of Foundation Models","created":"2024-11-03","cited_by_count":9,"area":null},{"id":"2412.02856","title":"Is Large-Scale Pretraining the Secret to Good Domain Generalization?","created":"2024-12-03","cited_by_count":9,"area":null},{"id":"2310.00987","title":"A Theoretical Analysis of the Test Error of Finite-Rank Kernel Ridge Regression","created":"2023-10-02","cited_by_count":8,"area":null},{"id":"2406.18864","title":"Learning Modality Knowledge Alignment for Cross-Modality Transfer","created":"2024-06-27","cited_by_count":8,"area":null},{"id":"2302.07778","title":"Measuring the Instability of Fine-Tuning","created":"2023-02-15","cited_by_count":7,"area":null},{"id":"2304.01489","title":"Improved Visual Fine-tuning with Natural Language Supervision","created":"2023-04-04","cited_by_count":7,"area":null},{"id":"2112.08772","title":"Sharpness-Aware Minimization with Dynamic Reweighting","created":"2021-12-16","cited_by_count":6,"area":null},{"id":"2206.05703","title":"PAC-Net: A Model Pruning Approach to Inductive Transfer Learning","created":"2022-06-12","cited_by_count":6,"area":null},{"id":"2210.09658","title":"ROSE: Robust Selective Fine-tuning for Pre-trained Language Models","created":"2022-10-18","cited_by_count":6,"area":null},{"id":"2304.00792","title":"Few-shot Fine-tuning is All You Need for Source-free Domain Adaptation","created":"2023-04-03","cited_by_count":6,"area":null},{"id":"2308.12058","title":"DR-Tune: Improving Fine-tuning of Pretrained Visual Models by Distribution Regularization with Semantic Calibration","created":"2023-08-23","cited_by_count":6,"area":null},{"id":"2110.07510","title":"Omni-Training: Bridging Pre-Training and Meta-Training for Few-Shot Learning","created":"2021-10-14","cited_by_count":5,"area":null},{"id":"2203.04668","title":"Towards Inadequately Pre-trained Models in Transfer Learning","created":"2022-03-09","cited_by_count":5,"area":null},{"id":"2204.08226","title":"Empirical Evaluation and Theoretical Analysis for Representation Learning: A Survey","created":"2022-04-18","cited_by_count":5,"area":null},{"id":"2206.03726","title":"Hub-Pathway: Transfer Learning from A Hub of Pre-trained Models","created":"2022-06-08","cited_by_count":5,"area":null},{"id":"2403.07282","title":"Enhancing Transfer Learning with Flexible Nonparametric Posterior Sampling","created":"2024-03-12","cited_by_count":5,"area":null},{"id":"2409.01341","title":"Enhancing Test Time Adaptation with Few-shot Guidance","created":"2024-09-02","cited_by_count":5,"area":null},{"id":"2505.21755","title":"FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering","created":"2025-05-27","cited_by_count":5,"area":null},{"id":"2112.11554","title":"Distribution-aware Margin Calibration for Semantic Segmentation in Images","created":"2021-12-21","cited_by_count":4,"area":null},{"id":"2204.12833","title":"Transfer Learning with Pre-trained Conditional Generative Models","created":"2022-04-27","cited_by_count":4,"area":null},{"id":"2302.01798","title":"Interpretations of Domain Adaptations via Layer Variational Analysis","created":"2023-02-03","cited_by_count":4,"area":null},{"id":"2302.08571","title":"A Comprehensive Review and a Taxonomy of Edge Machine Learning: Requirements, Paradigms, and Techniques","created":"2023-02-16","cited_by_count":4,"area":null},{"id":"2305.07892","title":"DAC-MR: Data Augmentation Consistency Based Meta-Regularization for Meta-Learning","created":"2023-05-13","cited_by_count":4,"area":null},{"id":"2305.09652","title":"The Interpreter Understands Your Meaning: End-to-end Spoken Language Understanding Aided by Speech Translation","created":"2023-05-16","cited_by_count":4,"area":null},{"id":"2310.08677","title":"GeSS: Benchmarking Geometric Deep Learning under Scientific Applications with Distribution Shifts","created":"2023-10-12","cited_by_count":4,"area":null},{"id":"2311.08782","title":"Language Semantic Graph Guided Data-Efficient Learning","created":"2023-11-15","cited_by_count":4,"area":null},{"id":"2402.08096","title":"An Efficient Rehearsal Scheme for Catastrophic Forgetting Mitigation during Multi-stage Fine-tuning","created":"2024-02-12","cited_by_count":4,"area":null},{"id":"2404.05207","title":"iVPT: Improving Task-relevant Information Sharing in Visual Prompt Tuning by Cross-layer Dynamic Connection","created":"2024-04-08","cited_by_count":4,"area":null},{"id":"2409.17663","title":"Explanation Bottleneck Models","created":"2024-09-26","cited_by_count":4,"area":null},{"id":"2411.01158","title":"Pin-Tuning: Parameter-Efficient In-Context Tuning for Few-Shot Molecular Property Prediction","created":"2024-11-02","cited_by_count":4,"area":null},{"id":"2103.13941","title":"SMILE: Self-Distilled MIxup for Efficient Transfer LEarning","created":"2021-03-25","cited_by_count":3,"area":null},{"id":"2104.00619","title":"Modular Adaptation for Cross-Domain Few-Shot Learning","created":"2021-04-01","cited_by_count":3,"area":null},{"id":"2210.09745","title":"Transfer learning with affine model transformation","created":"2022-10-18","cited_by_count":3,"area":null},{"id":"2211.16299","title":"Transferability Estimation Based On Principal Gradient Expectation","created":"2022-11-29","cited_by_count":3,"area":null},{"id":"2308.07728","title":"Domain-Aware Fine-Tuning: Enhancing Neural Network Adaptability","created":"2023-08-15","cited_by_count":3,"area":null},{"id":"2312.04032","title":"RoAST: Robustifying Language Models via Adversarial Perturbation with Selective Training","created":"2023-12-07","cited_by_count":3,"area":null},{"id":"2401.10220","title":"AutoFT: Learning an Objective for Robust Fine-Tuning","created":"2024-01-18","cited_by_count":3,"area":null},{"id":"2403.14114","title":"Test-time Similarity Modification for Person Re-identification toward Temporal Distribution Shift","created":"2024-03-21","cited_by_count":3,"area":null},{"id":"2404.11013","title":"Control Theoretic Approach to Fine-Tuning and Transfer Learning","created":"2024-04-17","cited_by_count":3,"area":null},{"id":"2405.20126","title":"Federated and Transfer Learning for Cancer Detection Based on Image Analysis","created":"2024-05-30","cited_by_count":3,"area":null},{"id":"2505.02604","title":"Connecting Independently Trained Modes via Layer-Wise Connectivity","created":"2025-05-05","cited_by_count":3,"area":null}],"next_cursor":"100"}