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Found 7 papers, 2 papers with code
Adaptive Random Feature Regularization on Fine-tuning Deep Neural Networks
To this end, AdaRand minimizes the gap between feature vectors and random reference vectors that are sampled from class conditional Gaussian distributions.
Partial Search in a Frozen Network is Enough to Find a Strong Lottery Ticket
In addition to reducing search space, the random freezing pattern can also be exploited to reduce model size in inference.
Revisiting Permutation Symmetry for Merging Models between Different Datasets
We also show that merged models require datasets for merging in order to achieve a high accuracy.
Transferring Learning Trajectories of Neural Networks
Training deep neural networks (DNNs) is computationally expensive, which is problematic especially when performing duplicated or similar training runs in model ensemble or fine-tuning pre-trained models, for example.
Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networks
Few-shot learning for neural networks (NNs) is an important problem that aims to train NNs with a few data.
Transfer Learning with Pre-trained Conditional Generative Models
To transfer source knowledge without these assumptions, we propose a transfer learning method that uses deep generative models and is composed of the following two stages: pseudo pre-training (PP) and pseudo semi-supervised learning (P-SSL).
Pruning Randomly Initialized Neural Networks with Iterative Randomization
Pruning the weights of randomly initialized neural networks plays an important role in the context of lottery ticket hypothesis.
