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 2176–2200 of 10307 papers

TitleStatusHype
A Competition Winning Deep Reinforcement Learning Agent in microRTSCode1
Text Detoxification as Style Transfer in English and HindiCode0
Comparative Analysis of ImageNet Pre-Trained Deep Learning Models and DINOv2 in Medical Imaging ClassificationCode0
MODIPHY: Multimodal Obscured Detection for IoT using PHantom Convolution-Enabled Faster YOLOCode1
Multi-Modal Emotion Recognition by Text, Speech and Video Using Pretrained Transformers—0
An Optimization Framework for Processing and Transfer Learning for the Brain Tumor SegmentationCode0
Should I try multiple optimizers when fine-tuning pre-trained Transformers for NLP tasks? Should I tune their hyperparameters?—0
Transfer learning with generative models for object detection on limited datasets—0
Embedding Compression for Teacher-to-Student Knowledge Transfer—0
Transferring facade labels between point clouds with semantic octrees while considering change detectionCode0
BarlowTwins-CXR : Enhancing Chest X-Ray abnormality localization in heterogeneous data with cross-domain self-supervised learning—0
Attention-Enhanced Prioritized Proximal Policy Optimization for Adaptive Edge Caching—0
Text-to-Code Generation with Modality-relative Pre-training—0
PLAPT: Protein-Ligand Binding Affinity Prediction Using Pretrained TransformersCode2
Impact of Dataset Properties on Membership Inference Vulnerability of Deep Transfer Learning—0
Group Distributionally Robust Dataset Distillation with Risk MinimizationCode1
Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph TransformersCode2
Scaling Laws for Downstream Task Performance of Large Language Models—0
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation ModelsCode1
Symbol Correctness in Deep Neural Networks Containing Symbolic Layers—0
Enhancing textual textbook question answering with large language models and retrieval augmented generationCode0
Constrained Decoding for Cross-lingual Label ProjectionCode0
Survival and grade of the glioma prediction using transfer learning—0
Pruner: A Speculative Exploration Mechanism to Accelerate Tensor Program TuningCode1
Enhancing the efficiency of protein language models with minimal wet-lab data through few-shot learning—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