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 201–225 of 10307 papers

TitleStatusHype
Bilevel Continual LearningCode1
BIOSCAN-5M: A Multimodal Dataset for Insect BiodiversityCode1
Benchmarking Detection Transfer Learning with Vision TransformersCode1
Enhanced Gaussian Process Dynamical Models with Knowledge Transfer for Long-term Battery Degradation ForecastingCode1
Bert4XMR: Cross-Market Recommendation with Bidirectional Encoder Representations from TransformerCode1
BirdSAT: Cross-View Contrastive Masked Autoencoders for Bird Species Classification and MappingCode1
AVocaDo: Strategy for Adapting Vocabulary to Downstream DomainCode1
Avatar Knowledge Distillation: Self-ensemble Teacher Paradigm with UncertaintyCode1
Accelerated wind farm yaw and layout optimisation with multi-fidelity deep transfer learning wake modelsCode1
A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning ProcessesCode1
BadMerging: Backdoor Attacks Against Model MergingCode1
Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement LearningCode1
Automatic identification of segmentation errors for radiotherapy using geometric learningCode1
A Whisper transformer for audio captioning trained with synthetic captions and transfer learningCode1
SentenceMIM: A Latent Variable Language ModelCode1
BARThez: a Skilled Pretrained French Sequence-to-Sequence ModelCode1
Bayesian Optimization with Automatic Prior Selection for Data-Efficient Direct Policy SearchCode1
AutoTune: Automatically Tuning Convolutional Neural Networks for Improved Transfer LearningCode1
Beyond Self-Supervision: A Simple Yet Effective Network Distillation Alternative to Improve BackbonesCode1
Beyond Semantic to Instance Segmentation: Weakly-Supervised Instance Segmentation via Semantic Knowledge Transfer and Self-RefinementCode1
1st Place Solution to Google Landmark Retrieval 2020Code1
Active Learning for Domain Adaptation: An Energy-Based ApproachCode1
Automatic Dialect Adaptation in Finnish and its Effect on Perceived CreativityCode1
AdaptGuard: Defending Against Universal Attacks for Model AdaptationCode1
Auxiliary Signal-Guided Knowledge Encoder-Decoder for Medical Report GenerationCode1
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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