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 1451–1475 of 10307 papers

TitleStatusHype
Common Voice: A Massively-Multilingual Speech CorpusCode1
MineGAN: effective knowledge transfer from GANs to target domains with few imagesCode1
Different Set Domain Adaptation for Brain-Computer Interfaces: A Label Alignment ApproachCode1
SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized OptimizationCode1
A Comprehensive Survey on Transfer LearningCode1
Contrastive Representation DistillationCode1
Hierarchical Transformers for Long Document ClassificationCode1
Generative Pre-Training for Speech with Autoregressive Predictive CodingCode1
Decoupling Representation and Classifier for Long-Tailed RecognitionCode1
PT-CoDE: Pre-trained Context-Dependent Encoder for Utterance-level Emotion RecognitionCode1
HuggingFace's Transformers: State-of-the-art Natural Language ProcessingCode1
FedMD: Heterogenous Federated Learning via Model DistillationCode1
Meta-Transfer Learning through Hard TasksCode1
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighterCode1
MutualNet: Adaptive ConvNet via Mutual Learning from Network Width and ResolutionCode1
The Woman Worked as a Babysitter: On Biases in Language GenerationCode1
Sentence-BERT: Sentence Embeddings using Siamese BERT-NetworksCode1
Learning to Discover Novel Visual Categories via Deep Transfer ClusteringCode1
Models Genesis: Generic Autodidactic Models for 3D Medical Image AnalysisCode1
VUSFA:Variational Universal Successor Features Approximator to Improve Transfer DRL for Target Driven Visual NavigationCode1
KNEEL: Knee Anatomical Landmark Localization Using Hourglass NetworksCode1
WinoGrande: An Adversarial Winograd Schema Challenge at ScaleCode1
Hello, It's GPT-2 -- How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue SystemsCode1
A Simple and Effective Approach to Automatic Post-Editing with Transfer LearningCode1
Fully automatic computer-aided mass detection and segmentation via pseudo-color mammograms and Mask R-CNNCode1
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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