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 5101–5125 of 10307 papers

TitleStatusHype
Maestro-U: Leveraging joint speech-text representation learning for zero supervised speech ASR—0
On effects of Knowledge Distillation on Transfer Learning—0
Depth Contrast: Self-Supervised Pretraining on 3DPM Images for Mining Material ClassificationCode0
6th Place Solution to Google Universal Image Embedding—0
Conditional Neural Processes for Molecules—0
A Transfer Learning Based Approach for Classification of COVID-19 and Pneumonia in CT Scan Imaging—0
Review Learning: Alleviating Catastrophic Forgetting with Generative Replay without Generator—0
Accelerating Transfer Learning with Near-Data Computation on Cloud Object StoresCode0
Aplicación de redes neuronales convolucionales profundas al diagnóstico asistido de la enfermedad de AlzheimerCode0
Injecting Domain Knowledge from Empirical Interatomic Potentials to Neural Networks for Predicting Material PropertiesCode0
Motion-related Artefact Classification Using Patch-based Ensemble and Transfer Learning in Cardiac MRICode0
Improving Transfer Learning with a Dual Image and Video Transformer for Multi-label Movie Trailer Genre ClassificationCode0
Transfer Deep Reinforcement Learning-based Large-scale V2G Continuous Charging Coordination with Renewable Energy Sources—0
Few-Shot Visual Question Generation: A Novel Task and Benchmark Datasets—0
Multi-Task Learning for Joint Semantic Role and Proto-Role Labeling—0
NSCGCN: A novel deep GCN model to diagnosis COVID-19—0
Text-Derived Knowledge Helps Vision: A Simple Cross-modal Distillation for Video-based Action AnticipationCode0
Cross-dataset COVID-19 Transfer Learning with Cough Detection, Cough Segmentation, and Data AugmentationCode0
Self-supervised video pretraining yields robust and more human-aligned visual representations—0
Entity Tracking via Effective Use of Multi-Task Learning Model and Mention-guided DecodingCode0
Federated Continual Learning for Text Classification via Selective Inter-client TransferCode0
Investigating Massive Multilingual Pre-Trained Machine Translation Models for Clinical Domain via Transfer Learning—0
Self-supervised Model Based on Masked Autoencoders Advance CT Scans Classification—0
Detect, Distill and Update: Learned DB Systems Facing Out of Distribution DataCode0
The Role of Exploration for Task Transfer in Reinforcement Learning—0
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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