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 9326–9350 of 10307 papers

TitleStatusHype
Biometric Fish Classification of Temperate Species Using Convolutional Neural Network with Squeeze-and-Excitation—0
Razmecheno: Named Entity Recognition from Digital Archive of Diaries “Prozhito”—0
Compression of Higher Order Ambisonics with Multichannel RVQGAN—0
Computational strategies for cross-species knowledge transfer and translational biomedicine—0
Computation and Data Efficient Backdoor Attacks—0
Bio-Measurements Estimation and Support in Knee Recovery through Machine Learning—0
Compute- and Memory-Efficient Reinforcement Learning with Latent Experience Replay—0
Computer-aided Diagnosis of Malaria through Transfer Learning using the ResNet50 Backbone—0
Computer-Aided Extraction of Select MRI Markers of Cerebral Small Vessel Disease: A Systematic Review—0
Computer-Aided Osteoporosis Diagnosis Using Transfer Learning with Enhanced Features from Stacked Deep Learning Modules—0
Computer Vision in the Food Industry: Accurate, Real-time, and Automatic Food Recognition with Pretrained MobileNetV2—0
Computing with Categories in Machine Learning—0
Biological neurons act as generalization filters in reservoir computing—0
Concept Drift Adaptation by Exploiting Historical Knowledge—0
Concept drift-tolerant transfer learning in dynamic environments.—0
Concept Formation and Alignment in Language Models: Bridging Statistical Patterns in Latent Space to Concept Taxonomy—0
Concept Transfer Learning for Adaptive Language Understanding—0
Conceptual Expansion Neural Architecture Search (CENAS)—0
r-BTN: Cross-domain Face Composite and Synthesis from Limited Facial Patches—0
Covariate-Elaborated Robust Partial Information Transfer with Conditional Spike-and-Slab Prior—0
CON: Continual Object Navigation via Data-Free Inter-Agent Knowledge Transfer in Unseen and Unfamiliar Places—0
Concrete Surface Crack Detection with Convolutional-based Deep Learning Models—0
Concurrent Discrimination and Alignment for Self-Supervised Feature Learning—0
Condensed Sample-Guided Model Inversion for Knowledge Distillation—0
Conditional Adapters: Parameter-efficient Transfer Learning with Fast Inference—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