SOTAVerified

Transfer Learning

Transfer Learning is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.

( Image credit: Subodh Malgonde )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5276–5300 of 10307 papers

TitleStatusHype
Rethinking Continual Learning for Autonomous Agents and Robots—0
Rethinking Efficient Tuning Methods from a Unified Perspective—0
Rethinking Evaluation Protocols of Visual Representations Learned via Self-supervised Learning—0
Rethinking Image-to-Video Adaptation: An Object-centric Perspective—0
Rethinking Importance Weighting for Transfer Learning—0
Rethinking Membership Inference Attacks Against Transfer Learning—0
Rethinking Query, Key, and Value Embedding in Vision Transformer under Tiny Model Constraints—0
Rethinking the Role of Operating Conditions for Learning-based Multi-condition Fault Diagnosis—0
Rethinking Transfer and Auxiliary Learning for Improving Audio Captioning Transformer—0
Rethinking Two Consensuses of the Transferability in Deep Learning—0
Retrieval-based Knowledge Transfer: An Effective Approach for Extreme Large Language Model Compression—0
Reuse and Adaptation for Entity Resolution through Transfer Learning—0
Reuse of Neural Modules for General Video Game Playing—0
Reusing Neural Speech Representations for Auditory Emotion Recognition—0
RevCD -- Reversed Conditional Diffusion for Generalized Zero-Shot Learning—0
Revealing economic facts: LLMs know more than they say—0
Revealing Fine Structures of the Retinal Receptive Field by Deep Learning Networks—0
Revealing Secrets From Pre-trained Models—0
Reverse Probing: Evaluating Knowledge Transfer via Finetuned Task Embeddings for Coreference Resolution—0
Reverse Transfer Learning: Can Word Embeddings Trained for Different NLP Tasks Improve Neural Language Models?—0
Review Learning: Alleviating Catastrophic Forgetting with Generative Replay without Generator—0
Review of Deep Representation Learning Techniques for Brain-Computer Interfaces and Recommendations—0
Revised Regularization for Efficient Continual Learning through Correlation-Based Parameter Update in Bayesian Neural Networks—0
Revisiting Classical Bagging with Modern Transfer Learning for On-the-fly Disaster Damage Detector—0
Revisiting Euclidean Alignment for Transfer Learning in EEG-Based Brain-Computer Interfaces—0
Show:102550
← PrevPage 212 of 413Next →
All datasetsOffice-Home100 sleep nights of 8 caregiversBanglaLekha Isolated DatasetCOCO70KITTI Object Tracking Evaluation 2012Retinal Fundus MultiDisease Image Dataset (RFMiD)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2—Unverified
2DFA-ENTAccuracy69.2—Unverified
3DFA-SAFNAccuracy69.1—Unverified
4EasyTLAccuracy63.3—Unverified
5MEDAAccuracy60.3—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23—Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65—Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74—Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95—Unverified