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 8551–8575 of 10307 papers

TitleStatusHype
TIE-KD: Teacher-Independent and Explainable Knowledge Distillation for Monocular Depth EstimationCode0
Two Methods for Domain Adaptation of Bilingual Tasks: Delightfully Simple and Broadly ApplicableCode0
Two-sample Testing Using Deep LearningCode0
Two Sides of the Same Coin: White-box and Black-box Attacks for Transfer LearningCode0
Network Collaborator: Knowledge Transfer Between Network Reconstruction and Community DetectionCode0
Zero-shot transfer for implicit discourse relation classificationCode0
Two-Stage Synthesis Networks for Transfer Learning in Machine ComprehensionCode0
What makes ImageNet good for transfer learning?Code0
TX-Ray: Quantifying and Explaining Model-Knowledge Transfer in (Un-)Supervised NLPCode0
With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual RepresentationsCode0
Visually Impaired Aid using Convolutional Neural Networks, Transfer Learning, and Particle Competition and CooperationCode0
TransCDR: a deep learning model for enhancing the generalizability of cancer drug response prediction through transfer learning and multimodal data fusion for drug representationCode0
UDAMA: Unsupervised Domain Adaptation through Multi-discriminator Adversarial Training with Noisy Labels Improves Cardio-fitness PredictionCode0
Transfer Learning for Performance Modeling of Configurable Systems: An Exploratory AnalysisCode0
Learning deep abdominal CT registration through adaptive loss weighting and synthetic data generationCode0
The Utility of Feature Reuse: Transfer Learning in Data-Starved RegimesCode0
UIR-LoRA: Achieving Universal Image Restoration through Multiple Low-Rank AdaptationCode0
Using convolutional neural networks for the classification of breast cancer imagesCode0
Transfer Learning for Performance Modeling of Configurable Systems: A Causal AnalysisCode0
ViC-MAE: Self-Supervised Representation Learning from Images and Video with Contrastive Masked AutoencodersCode0
Towards detection and classification of microscopic foraminifera using transfer learningCode0
Ultrasound Image Classification using ACGAN with Small Training DatasetCode0
Visual Translation Embedding Network for Visual Relation DetectionCode0
UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task DistillationCode0
Zero-Shot Rationalization by Multi-Task Transfer Learning from Question AnsweringCode0
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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