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 3551–3575 of 10307 papers

TitleStatusHype
Contrastive Alignment of Vision to Language Through Parameter-Efficient Transfer LearningCode1
Continual Learning in the Presence of Spurious Correlation—0
Bias mitigation techniques in image classification: fair machine learning in human heritage collections—0
GeoMIM: Towards Better 3D Knowledge Transfer via Masked Image Modeling for Multi-view 3D UnderstandingCode1
PFSL: Personalized & Fair Split Learning with Data & Label Privacy for thin clientsCode1
Transfer learning method in the problem of binary classification of chest X-raysCode0
Trainable Projected Gradient Method for Robust Fine-tuningCode1
Partial Network CloningCode1
A model is worth tens of thousands of examples—0
AdaptGuard: Defending Against Universal Attacks for Model AdaptationCode1
Denoising Diffusion Autoencoders are Unified Self-supervised LearnersCode1
LION: Implicit Vision Prompt Tuning—0
Efficient Computation Sharing for Multi-Task Visual Scene UnderstandingCode0
Neural Architecture Search for Effective Teacher-Student Knowledge Transfer in Language Models—0
Learning for Amalgamation: A Multi-Source Transfer Learning Framework For Sentiment ClassificationCode0
Deep Metric Learning for Unsupervised Remote Sensing Change DetectionCode1
Knowledge Transfer for Pseudo-code Generation from Low Resource Programming Language—0
Patch-Prompt Aligned Bayesian Prompt Tuning for Vision-Language Models—0
A Survey of Deep Visual Cross-Domain Few-Shot Learning—0
Imitation and Transfer Learning for LQG Control—0
Transfer Learning Based Diagnosis and Analysis of Lung Sound Aberrations—0
LRDB: LSTM Raw data DNA Base-caller based on long-short term models in an active learning environment—0
Investigating GANsformer: A Replication Study of a State-of-the-Art Image Generation Model—0
Clinical Concept and Relation Extraction Using Prompt-based Machine Reading Comprehension—0
Performance Embeddings: A Similarity-based Approach to Automatic Performance Optimization—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