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 2001–2025 of 10307 papers

TitleStatusHype
Characterizing and Avoiding Negative Transfer—0
Computational strategies for cross-species knowledge transfer and translational biomedicine—0
Computation and Data Efficient Backdoor Attacks—0
The (In)Effectiveness of Intermediate Task Training For Domain Adaptation and Cross-Lingual Transfer Learning—0
A physics-based domain adaptation framework for modelling and forecasting building energy systems—0
Computer-aided Diagnosis of Malaria through Transfer Learning using the ResNet50 Backbone—0
Channel-wise pruning of neural networks with tapering resource constraint—0
Computer-Aided Osteoporosis Diagnosis Using Transfer Learning with Enhanced Features from Stacked Deep Learning Modules—0
A Petri Dish for Histopathology Image Analysis—0
Computing with Categories in Machine Learning—0
Channel Scaling: A Scale-and-Select Approach for Transfer Learning—0
Change your singer: a transfer learning generative adversarial framework for song to song conversion—0
A Permutation-Invariant Representation of Neural Networks with Neuron Embeddings—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
Action Recognition for American Sign Language—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
Assessing the Performance of Analog Training for Transfer Learning—0
Adversarial Inductive Transfer Learning with input and output space adaptation—0
A Pathology-Based Machine Learning Method to Assist in Epithelial Dysplasia Diagnosis—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