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 226–250 of 10307 papers

TitleStatusHype
Boosting Weakly Supervised Object Detection with Progressive Knowledge TransferCode1
2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data SetsCode1
Breaking the Data Barrier -- Building GUI Agents Through Task GeneralizationCode1
Bridge Correlational Neural Networks for Multilingual Multimodal Representation LearningCode1
Bridge to Target Domain by Prototypical Contrastive Learning and Label Confusion: Re-explore Zero-Shot Learning for Slot FillingCode1
Adaptive Consistency Regularization for Semi-Supervised Transfer LearningCode1
BuildingsBench: A Large-Scale Dataset of 900K Buildings and Benchmark for Short-Term Load ForecastingCode1
1st Place Solution to Google Landmark Retrieval 2020Code1
Active Transfer Learning for Efficient Video-Specific Human Pose EstimationCode1
Automatic Dialect Adaptation in Finnish and its Effect on Perceived CreativityCode1
CALIP: Zero-Shot Enhancement of CLIP with Parameter-free AttentionCode1
Can LLMs' Tuning Methods Work in Medical Multimodal Domain?Code1
Can LLM Watermarks Robustly Prevent Unauthorized Knowledge Distillation?Code1
Auxiliary Signal-Guided Knowledge Encoder-Decoder for Medical Report GenerationCode1
Bag of Tricks for Image Classification with Convolutional Neural NetworksCode1
BiToD: A Bilingual Multi-Domain Dataset For Task-Oriented Dialogue ModelingCode1
AdaBoost-CNN: An adaptive boosting algorithm for convolutional neural networks to classify multi-class imbalanced datasets using transfer learningCode1
Chaos as an interpretable benchmark for forecasting and data-driven modellingCode1
Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric ModelsCode1
Chip Placement with Deep Reinforcement LearningCode1
Choquet Integral and Coalition Game-based Ensemble of Deep Learning Models for COVID-19 Screening from Chest X-ray ImagesCode1
Classification of animal sounds in a hyperdiverse rainforest using Convolutional Neural NetworksCode1
Classification of Epithelial Ovarian Carcinoma Whole-Slide Pathology Images Using Deep Transfer LearningCode1
Class-relation Knowledge Distillation for Novel Class DiscoveryCode1
AutoGCL: Automated Graph Contrastive Learning via Learnable View GeneratorsCode1
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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