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 851–900 of 10307 papers

TitleStatusHype
An Uncertainty-aware Transfer Learning-based Framework for Covid-19 DiagnosisCode1
Learning Invariant Representation for Continual LearningCode1
Learning Relation Prototype from Unlabeled Texts for Long-tail Relation ExtractionCode1
Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restorationCode1
A Strong and Simple Deep Learning Baseline for BCI MI DecodingCode1
Learning to Localize Actions from MomentsCode1
AnyMatch -- Efficient Zero-Shot Entity Matching with a Small Language ModelCode1
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighterCode1
CAREER: A Foundation Model for Labor Sequence DataCode1
AnyStar: Domain randomized universal star-convex 3D instance segmentationCode1
Learning Visual Representations for Transfer Learning by Suppressing TextureCode1
Learning with Alignments: Tackling the Inter- and Intra-domain Shifts for Cross-multidomain Facial Expression RecognitionCode1
AP-10K: A Benchmark for Animal Pose Estimation in the WildCode1
SentenceMIM: A Latent Variable Language ModelCode1
Cumulative Spatial Knowledge Distillation for Vision TransformersCode1
DAA: A Delta Age AdaIN operation for age estimation via binary code transformerCode1
CUDA: Convolution-based Unlearnable DatasetsCode1
Distilling Knowledge from Graph Convolutional NetworksCode1
Association Graph Learning for Multi-Task Classification with Category ShiftsCode1
Adversarially-Trained Deep Nets Transfer Better: Illustration on Image ClassificationCode1
A Closer Look at the Few-Shot Adaptation of Large Vision-Language ModelsCode1
A Study of Face Obfuscation in ImageNetCode1
Curriculum By SmoothingCode1
CytoImageNet: A large-scale pretraining dataset for bioimage transfer learningCode1
Distillation from Heterogeneous Models for Top-K RecommendationCode1
Distilling Image Classifiers in Object DetectorsCode1
Disentangled Knowledge Transfer for OOD Intent Discovery with Unified Contrastive LearningCode1
Lifelong Event Detection with Knowledge TransferCode1
Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent AlignmentCode1
Disentangled Pre-training for Human-Object Interaction DetectionCode1
Assemble Foundation Models for Automatic Code SummarizationCode1
Lipschitz Lifelong Reinforcement LearningCode1
MutualNet: Adaptive ConvNet via Mutual Learning from Network Width and ResolutionCode1
Unified Domain Adaptive Semantic SegmentationCode1
LLM-Neo: Parameter Efficient Knowledge Distillation for Large Language ModelsCode1
Load Forecasting for Households and Energy Communities: Are Deep Learning Models Worth the Effort?Code1
Adversarial Self-Supervised Contrastive LearningCode1
Data-Free Model ExtractionCode1
Long-Tailed Visual Recognition via Self-Heterogeneous Integration with Knowledge ExcavationCode1
LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge RecoveryCode1
Disentangling Spatial and Temporal Learning for Efficient Image-to-Video Transfer LearningCode1
Data Mining in Clinical Trial Text: Transformers for Classification and Question Answering TasksCode1
DDAM-PS: Diligent Domain Adaptive Mixer for Person SearchCode1
A Survey of Label-Efficient Deep Learning for 3D Point CloudsCode1
Neuro2Semantic: A Transfer Learning Framework for Semantic Reconstruction of Continuous Language from Human Intracranial EEGCode1
DeepDarts: Modeling Keypoints as Objects for Automatic Scorekeeping in Darts using a Single CameraCode1
Decoupling Representation and Classifier for Long-Tailed RecognitionCode1
MA-LoT: Multi-Agent Lean-based Long Chain-of-Thought Reasoning enhances Formal Theorem ProvingCode1
ASSET: Robust Backdoor Data Detection Across a Multiplicity of Deep Learning ParadigmsCode1
Diffusion Models Beat GANs on Image ClassificationCode1
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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