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 1951–1975 of 10307 papers

TitleStatusHype
Accuracy enhancement method for speech emotion recognition from spectrogram using temporal frequency correlation and positional information learning through knowledge transferCode1
Leveraging Near-Field Lighting for Monocular Depth Estimation from Endoscopy Videos—0
To Supervise or Not to Supervise: Understanding and Addressing the Key Challenges of Point Cloud Transfer Learning—0
The Need for Speed: Pruning Transformers with One RecipeCode1
Advancing Extrapolative Predictions of Material Properties through Learning to Learn—0
Task2Box: Box Embeddings for Modeling Asymmetric Task RelationshipsCode0
Can Machine Translation Bridge Multilingual Pretraining and Cross-lingual Transfer Learning?—0
Engagement Measurement Based on Facial Landmarks and Spatial-Temporal Graph Convolutional Networks—0
Exploring CausalWorld: Enhancing robotic manipulation via knowledge transfer and curriculum learning—0
A Hybrid Approach To Aspect Based Sentiment Analysis Using Transfer Learning—0
Self-Supervised Learning for Medical Image Data with Anatomy-Oriented Imaging Planes—0
Enhancing Industrial Transfer Learning with Style Filter: Cost Reduction and Defect-Focus—0
Exploit High-Dimensional RIS Information to Localization: What Is the Impact of Faulty Element?—0
GoodSAM: Bridging Domain and Capacity Gaps via Segment Anything Model for Distortion-aware Panoramic Semantic Segmentation—0
Grammatical vs Spelling Error Correction: An Investigation into the Responsiveness of Transformer-based Language Models using BART and MarianMT—0
Transfer Learning of Real Image Features with Soft Contrastive Loss for Fake Image Detection—0
Employing High-Dimensional RIS Information for RIS-aided Localization Systems—0
Enhancing Visual Continual Learning with Language-Guided Supervision—0
MatchSeg: Towards Better Segmentation via Reference Image MatchingCode1
An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated LearningCode2
A Deep Learning Architectures for Kidney Disease Classification—0
VLUE: A New Benchmark and Multi-task Knowledge Transfer Learning for Vietnamese Natural Language Understanding—0
SiMBA: Simplified Mamba-Based Architecture for Vision and Multivariate Time seriesCode3
Vehicle Detection Performance in Nordic Region—0
Not All Attention is Needed: Parameter and Computation Efficient Transfer Learning for Multi-modal Large Language ModelsCode0
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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