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Found 10 papers, 5 papers with code
Unsupervised Visual Representation Learning via Mutual Information Regularized Assignment
This paper proposes Mutual Information Regularized Assignment (MIRA), a pseudo-labeling algorithm for unsupervised representation learning inspired by information maximization.
Test-Time Adaptation via Self-Training with Nearest Neighbor Information
To overcome this limitation, we propose a novel test-time adaptation method, called Test-time Adaptation via Self-Training with nearest neighbor information (TAST), which is composed of the following procedures: (1) adds trainable adaptation modules on top of the trained feature extractor; (2) newly defines a pseudo-label distribution for the test data by using the nearest neighbor information; (3) trains these modules only a few times during test time to match the nearest neighbor-based pseudo label distribution and a prototype-based class distribution for the test data; and (4) predicts the label of test data using the average predicted class distribution from these modules.
Few-Example Clustering via Contrastive Learning
We propose Few-Example Clustering (FEC), a novel algorithm that performs contrastive learning to cluster few examples.
Unsupervised Embedding Adaptation via Early-Stage Feature Reconstruction for Few-Shot Classification
We propose unsupervised embedding adaptation for the downstream few-shot classification task.
Improving Generalization in Meta-RL with Imaginary Tasks from Latent Dynamics Mixture
By training a policy on mixture tasks along with original training tasks, LDM allows the agent to prepare for unseen test tasks during training and prevents the agent from overfitting the training tasks.
Novelty Detection Via Blurring
Conventional out-of-distribution (OOD) detection schemes based on variational autoencoder or Random Network Distillation (RND) have been observed to assign lower uncertainty to the OOD than the target distribution.
Robust Training with Ensemble Consensus
Since deep neural networks are over-parameterized, they can memorize noisy examples.
Fourier Phase Retrieval with Extended Support Estimation via Deep Neural Network
We consider the problem of sparse phase retrieval from Fourier transform magnitudes to recover the $k$-sparse signal vector and its support $\mathcal{T}$.
Tree Search Network for Sparse Regression
We consider the classical sparse regression problem of recovering a sparse signal $x_0$ given a measurement vector $y = \Phi x_0+w$.
Sample-Efficient Deep Reinforcement Learning via Episodic Backward Update
We propose Episodic Backward Update (EBU) - a novel deep reinforcement learning algorithm with a direct value propagation.
