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 2051–2075 of 10307 papers

TitleStatusHype
AnyTouch: Learning Unified Static-Dynamic Representation across Multiple Visuo-tactile Sensors—0
CellLineNet: End-to-End Learning and Transfer Learning For Multiclass Epithelial Breast cell Line Classification via a Convolutional Neural Network—0
Adversarial Imitation via Variational Inverse Reinforcement Learning—0
Cross-Modal Knowledge Transfer via Inter-Modal Translation and Alignment for Affect Recognition—0
CellCentroidFormer: Combining Self-attention and Convolution for Cell Detection—0
Deep Feature Learning from a Hospital-Scale Chest X-ray Dataset with Application to TB Detection on a Small-Scale Dataset—0
Any-Shot Sequential Anomaly Detection in Surveillance Videos—0
CEHR-BERT: Incorporating temporal information from structured EHR data to improve prediction tasks—0
Adversarial Fine-tune with Dynamically Regulated Adversary—0
Cross-modal Knowledge Transfer Learning as Graph Matching Based on Optimal Transport for ASR—0
Cross-Modal Knowledge Transfer Without Task-Relevant Source Data—0
CDS: Cross-Domain Self-Supervised Pre-Training—0
CDR-Adapter: Learning Adapters to Dig Out More Transferring Ability for Cross-Domain Recommendation Models—0
CDKT-FL: Cross-Device Knowledge Transfer using Proxy Dataset in Federated Learning—0
CDCGen: Cross-Domain Conditional Generation via Normalizing Flows and Adversarial Training—0
Adversarial Feature Training for Generalizable Robotic Visuomotor Control—0
CCT-Net: Category-Invariant Cross-Domain Transfer for Medical Single-to-Multiple Disease Diagnosis—0
CCS-GAN: COVID-19 CT-scan classification with very few positive training images—0
An X3D Neural Network Analysis for Runner's Performance Assessment in a Wild Sporting Environment—0
CB-HVTNet: A channel-boosted hybrid vision transformer network for lymphocyte assessment in histopathological images—0
Adversarial Domain Adaptation Being Aware of Class Relationships—0
Bias and Generalizability of Foundation Models across Datasets in Breast Mammography—0
Crossmodal Knowledge Distillation with WordNet-Relaxed Text Embeddings for Robust Image Classification—0
Cross-Modal Multi-Tasking for Speech-to-Text Translation via Hard Parameter Sharing—0
Cause-Effect Preservation and Classification using Neurochaos Learning—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