Search Results for author:
Found 7 papers, 2 papers with code
ContraCluster: Learning to Classify without Labels by Contrastive Self-Supervision and Prototype-Based Semi-Supervision
The recent advances in representation learning inspire us to take on the challenging problem of unsupervised image classification tasks in a principled way.
Transferring Pre-trained Multimodal Representations with Cross-modal Similarity Matching
Despite surprising performance on zero-shot transfer, pre-training a large-scale multimodal model is often prohibitive as it requires a huge amount of data and computing resources.
SelfMatch: Combining Contrastive Self-Supervision and Consistency for Semi-Supervised Learning
This paper introduces SelfMatch, a semi-supervised learning method that combines the power of contrastive self-supervised learning and consistency regularization.
POMO: Policy Optimization with Multiple Optima for Reinforcement Learning
We introduce Policy Optimization with Multiple Optima (POMO), an end-to-end approach for building such a heuristic solver.
VaB-AL: Incorporating Class Imbalance and Difficulty with Variational Bayes for Active Learning
We show that our method can be applied to classification tasks on multiple different datasets -- including one that is a real-world dataset with heavy data imbalance -- significantly outperforming the state of the art.
DefogGAN: Predicting Hidden Information in the StarCraft Fog of War with Generative Adversarial Nets
We propose DefogGAN, a generative approach to the problem of inferring state information hidden in the fog of war for real-time strategy (RTS) games.
Unsupervised Visual Attribute Transfer with Reconfigurable Generative Adversarial Networks
To address the issue, we propose an unsupervised method to learn to transfer visual attribute.
