19 citations
5 papers
Contrastive Neural Processes for Self-Supervised Learning
Konstantinos Kallidromitis, Denis Gudovskiy, Kazuki Kozuka +2
Recent contrastive methods show significant improvement in self-supervised learning in several domains. In particular, contrastive methods are most effective where data augmentatio…
CutDepth:Edge-aware Data Augmentation in Depth Estimation
Yasunori Ishii, Takayoshi Yamashita
It is difficult to collect data on a large scale in a monocular depth estimation because the task requires the simultaneous acquisition of RGB images and depths. Data augmentation…
An Analysis of Frame-skipping in Reinforcement Learning
Shivaram Kalyanakrishnan, Siddharth Aravindan, Vishwajeet Bagdawat +5
In the practice of sequential decision making, agents are often designed to sense state at regular intervals of time steps, , ignoring state information in between sensi…
PlaNet of the Bayesians: Reconsidering and Improving Deep Planning Network by Incorporating Bayesian Inference
Masashi Okada, Norio Kosaka, Tadahiro Taniguchi
In the present paper, we propose an extension of the Deep Planning Network (PlaNet), also referred to as PlaNet of the Bayesians (PlaNet-Bayes). There has been a growing demand in…
Deep Face Recognition Model Compression via Knowledge Transfer and Distillation
Jayashree Karlekar, Jiashi Feng, Zi Sian Wong +1
Fully convolutional networks (FCNs) have become de facto tool to achieve very high-level performance for many vision and non-vision tasks in general and face recognition in particu…