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 851–875 of 10307 papers

TitleStatusHype
Search for temporal cell segmentation robustness in phase-contrast microscopy videosCode1
CLIP-Lite: Information Efficient Visual Representation Learning with Language SupervisionCode1
Exploring Neural Models for Query-Focused SummarizationCode1
VL-Adapter: Parameter-Efficient Transfer Learning for Vision-and-Language TasksCode1
Multinational Address Parsing: A Zero-Shot EvaluationCode1
Handwritten Mathematical Expression Recognition via Attention Aggregation based Bi-directional Mutual LearningCode1
SemanticStyleGAN: Learning Compositional Generative Priors for Controllable Image Synthesis and EditingCode1
TransBoost: A Boosting-Tree Kernel Transfer Learning Algorithm for Improving Financial InclusionCode1
Transferring Unconditional to Conditional GANs with Hyper-ModulationCode1
PointCLIP: Point Cloud Understanding by CLIPCode1
A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled SamplesCode1
Self-Supervised Material and Texture Representation Learning for Remote Sensing TasksCode1
Active Learning for Domain Adaptation: An Energy-Based ApproachCode1
PartImageNet: A Large, High-Quality Dataset of PartsCode1
OW-DETR: Open-world Detection TransformerCode1
Contrastive Cross-domain Recommendation in MatchingCode1
Slow Learning and Fast Inference: Efficient Graph Similarity Computation via Knowledge DistillationCode1
Domain Adaptation with Invariant Representation Learning: What Transformations to Learn?Code1
SPATL: Salient Parameter Aggregation and Transfer Learning for Heterogeneous Clients in Federated LearningCode1
Towards Robust and Adaptive Motion Forecasting: A Causal Representation PerspectiveCode1
Classification of animal sounds in a hyperdiverse rainforest using Convolutional Neural NetworksCode1
Transfer Learning with Jukebox for Music Source SeparationCode1
VL-LTR: Learning Class-wise Visual-Linguistic Representation for Long-Tailed Visual RecognitionCode1
Domain Prompt Learning for Efficiently Adapting CLIP to Unseen DomainsCode1
CytoImageNet: A large-scale pretraining dataset for bioimage transfer learningCode1
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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