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 3651–3675 of 10307 papers

TitleStatusHype
Cross-lingual Knowledge Transfer via Distillation for Multilingual Information Retrieval—0
TransferD2: Automated Defect Detection Approach in Smart Manufacturing using Transfer Learning Techniques—0
Choice Fusion as Knowledge for Zero-Shot Dialogue State TrackingCode0
Forecasting Workload in Cloud Computing: Towards Uncertainty-Aware Predictions and Transfer LearningCode1
Adapting Pre-trained Language Models for Quantum Natural Language Processing—0
HUST bearing: a practical dataset for ball bearing fault diagnosis—0
Pre-Finetuning for Few-Shot Emotional Speech RecognitionCode0
A Comprehensive Survey on Source-free Domain Adaptation—0
Automated Extraction of Fine-Grained Standardized Product Information from Unstructured Multilingual Web Data—0
Semantic-Fused Multi-Granularity Cross-City Traffic PredictionCode1
ACE: Zero-Shot Image to Image Translation via Pretrained Auto-Contrastive-EncoderCode0
Transfer Learning Enhanced Full Waveform Inversion—0
KS-DETR: Knowledge Sharing in Attention Learning for Detection TransformerCode0
Modular Deep Learning—0
Steerable Equivariant Representation Learning—0
ASSET: Robust Backdoor Data Detection Across a Multiplicity of Deep Learning ParadigmsCode1
A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT—0
SF2Former: Amyotrophic Lateral Sclerosis Identification From Multi-center MRI Data Using Spatial and Frequency Fusion TransformerCode2
SU-Net: Pose estimation network for non-cooperative spacecraft on-orbitCode0
On Inductive Biases for Machine Learning in Data Constrained SettingsCode0
Boosting Convolutional Neural Networks' Protein Binding Site Prediction Capacity Using SE(3)-invariant transformers, Transfer Learning and Homology-based Augmentation—0
Multiobjective Evolutionary Pruning of Deep Neural Networks with Transfer Learning for improving their Performance and Robustness—0
Exploring the Limits of Transfer Learning with Unified Model in the Cybersecurity Domain—0
Few-shot Multimodal Multitask Multilingual Learning—0
HomoDistil: Homotopic Task-Agnostic Distillation of Pre-trained Transformers—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