This co-design of self-supervised learning techniques and architectural improvement results in a new model family called ConvNeXt V2, which significantly improves the performance of pure ConvNets on various recognition benchmarks, including ImageNet classification, COCO detection, and ADE20K segmentation.
Ranked #45 on Semantic Segmentation on ADE20K
Masked Autoencoding (MAE) has emerged as an effective approach for pre-training representations across multiple domains.
Key to our approach is a local implicit neural representation built on ray-voxel pairs that allows our method to generalize to unseen objects and achieve fast inference speed.
Synthetic data is emerging as a promising solution to the scalability issue of supervised deep learning, especially when real data are difficult to acquire or hard to annotate.
However, neural network models trained on synthetic data, do not perform well on real data because of the domain gap.
Disentanglement learning is crucial for obtaining disentangled representations and controllable generation.
A new mechanism for efficiently solving the Markov decision processes (MDPs) is proposed in this paper.
Then, we leverage this approximate model along with a notion of reachability using Mean First Passage Times to perform Model-Based reinforcement learning.
The solution convergence of Markov Decision Processes (MDPs) can be accelerated by prioritized sweeping of states ranked by their potential impacts to other states.