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 4601–4625 of 10307 papers

TitleStatusHype
Histology Virtual Staining with Mask-Guided Adversarial Transfer Learning for Tertiary Lymphoid Structure Detection—0
HistoTransfer: Understanding Transfer Learning for Histopathology—0
HMAE: Self-Supervised Few-Shot Learning for Quantum Spin Systems—0
Holistic Multi-Slice Framework for Dynamic Simultaneous Multi-Slice MRI Reconstruction—0
HoloFed: Environment-Adaptive Positioning via Multi-band Reconfigurable Holographic Surfaces and Federated Learning—0
HomoDistil: Homotopic Task-Agnostic Distillation of Pre-trained Transformers—0
Homomorphisms Between Transfer, Multi-Task, and Meta-Learning Systems—0
Homophily and missing links in citation networks—0
Hot PATE: Private Aggregation of Distributions for Diverse Task—0
On Transfer of Adversarial Robustness from Pretraining to Downstream Tasks—0
How Can Cross-lingual Knowledge Contribute Better to Fine-Grained Entity Typing?—0
How Can We Accelerate Progress Towards Human-like Linguistic Generalization?—0
How Different Text-preprocessing Techniques Using The BERT Model Affect The Gender Profiling of Authors—0
How Does Adversarial Fine-Tuning Benefit BERT?—0
How does a Multilingual LM Handle Multiple Languages?—0
How does Architecture Influence the Base Capabilities of Pre-trained Language Models? A Case Study Based on FFN-Wider and MoE Transformers—0
How Does Data Diversity Shape the Weight Landscape of Neural Networks?—0
Model-guided Multi-path Knowledge Aggregation for Aerial Saliency Prediction—0
How Effective is Pre-training of Large Masked Autoencoders for Downstream Earth Observation Tasks?—0
How Far Can We Go with Data Selection? A Case Study on Semantic Sequence Tagging Tasks—0
How Lightweight Can A Vision Transformer Be—0
How many Observations are Enough? Knowledge Distillation for Trajectory Forecasting—0
How much data do I need? A case study on medical data—0
How Much Off-The-Shelf Knowledge Is Transferable From Natural Images To Pathology Images?—0
How Self-Supervised Learning Can be Used for Fine-Grained Head Pose Estimation?—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