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

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Showing 1576–1600 of 10307 papers

TitleStatusHype
On Giant's Shoulders: Effortless Weak to Strong by Dynamic Logits Fusion—0
Self-Distillation Prototypes Network: Learning Robust Speaker Representations without Supervision—0
UniGLM: Training One Unified Language Model for Text-Attributed Graph EmbeddingCode1
Large Scale Transfer Learning for Tabular Data via Language ModelingCode2
Faces of Experimental Pain: Transferability of Deep Learned Heat Pain Features to Electrical Pain—0
Self-Regulated Data-Free Knowledge Amalgamation for Text Classification—0
Federated Learning Optimization: A Comparative Study of Data and Model Exchange Strategies in Dynamic Networks—0
Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutions—0
A-I-RAVEN and I-RAVEN-Mesh: Two New Benchmarks for Abstract Visual Reasoning—0
DP-MemArc: Differential Privacy Transfer Learning for Memory Efficient Language Models—0
Leveraging Foundation Models for Multi-modal Federated Learning with Incomplete Modality—0
ShareLoRA: Parameter Efficient and Robust Large Language Model Fine-tuning via Shared Low-Rank AdaptationCode0
Physics-Informed Deep Learning and Partial Transfer Learning for Bearing Fault Diagnosis in the Presence of Highly Missing Data—0
A Unified View of Abstract Visual Reasoning Problems—0
On the Effectiveness of Supervision in Asymmetric Non-Contrastive LearningCode0
ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers under Domain Shifts—0
A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges—0
Augmenting Biomedical Named Entity Recognition with General-domain ResourcesCode0
ADSNet: Cross-Domain LTV Prediction with an Adaptive Siamese Network in Advertising—0
Self-Supervised Representation Learning with Spatial-Temporal Consistency for Sign Language RecognitionCode1
Deep Learning Models to Automate the Scoring of Hand Radiographs for Rheumatoid Arthritis—0
Comparison of fine-tuning strategies for transfer learning in medical image classification—0
Industrial Language-Image Dataset (ILID): Adapting Vision Foundation Models for Industrial SettingsCode1
RASPNet: A Benchmark Dataset for Radar Adaptive Signal Processing Applications—0
UniBridge: A Unified Approach to Cross-Lingual Transfer Learning for Low-Resource LanguagesCode0
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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