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 10276–10300 of 10307 papers

TitleStatusHype
Task2Box: Box Embeddings for Modeling Asymmetric Task RelationshipsCode0
Task Embedding Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time-Series ForecastCode0
Taskonomy: Disentangling Task Transfer LearningCode0
TaxaDiffusion: Progressively Trained Diffusion Model for Fine-Grained Species GenerationCode0
Teaching Llama a New Language Through Cross-Lingual Knowledge TransferCode0
Teaching Wav2Vec2 the Language of the BrainCode0
TeamUNCC@LT-EDI-EACL2021: Hope Speech Detection using Transfer Learning with TransformersCode0
Technical Question Answering across Tasks and DomainsCode0
Technical Report: Combining knowledge from Transfer Learning during training and Wide ResnetsCode0
Temporal Relations of Informative Frames in Action RecognitionCode0
Tensor Analysis with n-Mode Generalized Difference SubspaceCode0
TernausNetV2: Fully Convolutional Network for Instance SegmentationCode0
Testing Large Language Models on Compositionality and Inference with Phrase-Level Adjective-Noun EntailmentCode0
Test time Adaptation through Perturbation RobustnessCode0
Text Detoxification as Style Transfer in English and HindiCode0
Text-Driven Foley Sound Generation With Latent Diffusion ModelCode0
Text Length Adaptation in Sentiment ClassificationCode0
TextWorld: A Learning Environment for Text-based GamesCode0
TGDM: Target Guided Dynamic Mixup for Cross-Domain Few-Shot LearningCode0
TGG: Transferable Graph Generation for Zero-shot and Few-shot LearningCode0
ThanosNet: A Novel Trash Classification Method Using MetadataCode0
The Arcade Learning Environment: An Evaluation Platform for General AgentsCode0
The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine LearningCode0
The iMaterialist Fashion Attribute DatasetCode0
The Impact of Model Zoo Size and Composition on Weight Space LearningCode0
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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