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 1026–1050 of 10307 papers

TitleStatusHype
Distilling Image Classifiers in Object DetectorsCode1
Equivariant Graph Neural Networks for 3D Macromolecular StructureCode1
Investigating Transfer Learning in Multilingual Pre-trained Language Models through Chinese Natural Language InferenceCode1
BiToD: A Bilingual Multi-Domain Dataset For Task-Oriented Dialogue ModelingCode1
Materials Representation and Transfer Learning for Multi-Property PredictionCode1
Aligning Pretraining for Detection via Object-Level Contrastive LearningCode1
Template-Based Named Entity Recognition Using BARTCode1
Syntax-augmented Multilingual BERT for Cross-lingual TransferCode1
MathBERT: A Pre-trained Language Model for General NLP Tasks in Mathematics EducationCode1
OpenBox: A Generalized Black-box Optimization ServiceCode1
Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue StateTrackingCode1
Effect of Pre-Training Scale on Intra- and Inter-Domain Full and Few-Shot Transfer Learning for Natural and Medical X-Ray Chest ImagesCode1
Transfer Learning for Sequence Generation: from Single-source to Multi-sourceCode1
Byakto Speech: Real-time long speech synthesis with convolutional neural network: Transfer learning from English to BanglaCode1
NeuralWOZ: Learning to Collect Task-Oriented Dialogue via Model-Based SimulationCode1
Knowledge Inheritance for Pre-trained Language ModelsCode1
Cross-Lingual Abstractive Summarization with Limited Parallel ResourcesCode1
Predicting Aqueous Solubility of Organic Molecules Using Deep Learning Models with Varied Molecular RepresentationsCode1
DeepGaze IIE: Calibrated prediction in and out-of-domain for state-of-the-art saliency modelingCode1
Towards Compact Single Image Super-Resolution via Contrastive Self-distillationCode1
TransNAS-Bench-101: Improving Transferability and Generalizability of Cross-Task Neural Architecture SearchCode1
Multi-Type-TD-TSR -- Extracting Tables from Document Images using a Multi-stage Pipeline for Table Detection and Table Structure Recognition: from OCR to Structured Table RepresentationsCode1
MIASSR: An Approach for Medical Image Arbitrary Scale Super-ResolutionCode1
Generalized Few-Shot Object Detection without ForgettingCode1
DeepDarts: Modeling Keypoints as Objects for Automatic Scorekeeping in Darts using a Single CameraCode1
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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