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 7851–7875 of 10307 papers

TitleStatusHype
Transcending Controlled Environments Assessing the Transferability of ASRRobust NLU Models to Real-World Applications—0
Transductive Adaptation of Black Box Predictions—0
Transductive Label Augmentation for Improved Deep Network Learning—0
Transductive Multi-class and Multi-label Zero-shot Learning—0
Transductive Multi-view Zero-Shot Learning—0
Transductive Zero-Shot Hashing via Coarse-to-Fine Similarity Mining—0
Transductive Zero-Shot Learning with a Self-training dictionary approach—0
Transferability analysis of data-driven additive manufacturing knowledge: a case study between powder bed fusion and directed energy deposition—0
Transferability Estimation Based On Principal Gradient Expectation—0
Transferability Estimation for Semantic Segmentation Task—0
Transferability Estimation using Bhattacharyya Class Separability—0
Transferability-Guided Cross-Domain Cross-Task Transfer Learning—0
Transferability in Deep Learning: A Survey—0
Transferability Metrics for Selecting Source Model Ensembles—0
Unsupervised Random Walk Sentence Embeddings: A Strong but Simple Baseline—0
Unsupervised Representation Learning by Invariance Propagation—0
Unsupervised Representation Learning by Discovering Reliable Image Relations—0
Unsupervised Representation Learning with Future Observation Prediction for Speech Emotion Recognition—0
Unsupervised Representations Improve Supervised Learning in Speech Emotion Recognition—0
Unsupervised Representation Transfer for Small Networks: I Believe I Can Distill On-the-Fly—0
Unsupervised state representation learning with robotic priors: a robustness benchmark—0
Unsupervised Transfer Learning in Multilingual Neural Machine Translation with Cross-Lingual Word Embeddings—0
Unsupervised Transfer Learning via Adversarial Contrastive Training—0
Unsupervised Transfer Learning via BERT Neuron Selection—0
Unsupervised Transfer Learning with Self-Supervised Remedy—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