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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3501–3525 of 10307 papers

TitleStatusHype
Concrete Surface Crack Detection with Convolutional-based Deep Learning Models—0
CON: Continual Object Navigation via Data-Free Inter-Agent Knowledge Transfer in Unseen and Unfamiliar Places—0
A Situated Dialogue System for Learning Structural Concepts in Blocks World—0
A General Multi-Task Learning Framework to Leverage Text Data for Speech to Text Tasks—0
Covariate-Elaborated Robust Partial Information Transfer with Conditional Spike-and-Slab Prior—0
Conceptual Expansion Neural Architecture Search (CENAS)—0
Concept Transfer Learning for Adaptive Language Understanding—0
A Simple yet Effective Joint Training Method for Cross-Lingual Universal Dependency Parsing—0
A General Multiple Data Augmentation Based Framework for Training Deep Neural Networks—0
Adaptation and Re-Identification Network: An Unsupervised Deep Transfer Learning Approach to Person Re-Identification—0
2nd Place Solution to ECCV 2020 VIPriors Object Detection Challenge—0
Concept Formation and Alignment in Language Models: Bridging Statistical Patterns in Latent Space to Concept Taxonomy—0
Concept drift-tolerant transfer learning in dynamic environments.—0
Concept Drift Adaptation by Exploiting Historical Knowledge—0
A general method for regularizing tensor decomposition methods via pseudo-data—0
Computing with Categories in Machine Learning—0
Computer Vision in the Food Industry: Accurate, Real-time, and Automatic Food Recognition with Pretrained MobileNetV2—0
Computer-Aided Osteoporosis Diagnosis Using Transfer Learning with Enhanced Features from Stacked Deep Learning Modules—0
Computer-Aided Extraction of Select MRI Markers of Cerebral Small Vessel Disease: A Systematic Review—0
A generalized machine learning framework for brittle crack problems using transfer learning and graph neural networks—0
Adapt and Align to Improve Zero-Shot Sketch-Based Image Retrieval—0
Computer-aided Diagnosis of Malaria through Transfer Learning using the ResNet50 Backbone—0
Compute- and Memory-Efficient Reinforcement Learning with Latent Experience Replay—0
Computation and Data Efficient Backdoor Attacks—0
A General Class of Transfer Learning Regression without Implementation Cost—0
Show:102550
← PrevPage 141 of 413Next →
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