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 6376–6400 of 10307 papers

TitleStatusHype
Overcoming Label Ambiguity with Multi-label Iterated Learning—0
Measuring the Effectiveness of Self-Supervised Learning using Calibrated Learning Curves—0
Structure-Aware Transformer Policy for Inhomogeneous Multi-Task Reinforcement Learning—0
Head2Toe: Utilizing Intermediate Representations for Better OOD Generalization—0
A Permutation-Invariant Representation of Neural Networks with Neuron Embeddings—0
Fundamental Limits of Transfer Learning in Binary Classifications—0
Pairwise Adversarial Training for Unsupervised Class-imbalanced Domain Adaptation—0
Dictionary Learning Under Generative Coefficient Priors with Applications to Compression—0
Generalisation in Lifelong Reinforcement Learning through Logical Composition—0
MetaHistoSeg: A Python Framework for Meta Learning in Histopathology Image Segmentation—0
A theoretically grounded characterization of feature representations—0
Early-Stopping for Meta-Learning: Estimating Generalization from the Activation Dynamics—0
A Semi-supervised Deep Transfer Learning Approach for Rolling-Element Bearing Remaining Useful Life PredictionCode0
The Details Matter: Preventing Class Collapse in Supervised Contrastive Learning—0
How to Adapt Your Large-Scale Vision-and-Language Model—0
Provably Robust Transfer—0
Text-Driven Image Manipulation via Semantic-Aware Knowledge Transfer—0
A Scaling Law for Syn-to-Real Transfer: How Much Is Your Pre-training Effective?—0
Representation Topology Divergence: A Method for Comparing Neural Network Representations.—0
MOBA: Multi-teacher Model Based Reinforcement Learning—0
Coherent and Consistent Relational Transfer Learning with Autoencoders—0
ConVAEr: Convolutional Variational AutoEncodeRs for incremental similarity learning—0
Partially Relaxed Masks for Lightweight Knowledge Transfer without Forgetting in Continual Learning—0
Selective Token Generation for Few-shot Language Modeling—0
Multi-batch Reinforcement Learning via Sample Transfer and Imitation 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