Author name cluster

Shikhar Sharma

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

1 author row

AAAI Conference 2021 Conference Paper

Object-Centric Image Generation from Layouts

Tristan Sylvain
Pengchuan Zhang
Yoshua Bengio
R Devon Hjelm
Shikhar Sharma

We begin with the hypothesis that a model must be able to understand individual objects and relationships between objects in order to generate complex scenes with multiple objects well. Our layout-to-image-generation method, which we call Object-Centric Generative Adversarial Network (or OC-GAN), relies on a novel Scene-Graph Similarity Module (SGSM). The SGSM learns representations of the spatial relationships between objects in the scene, which lead to our model’s improved layout-fidelity. We also propose changes to the conditioning mechanism of the generator that enhance its object instance-awareness. Apart from improving image quality, our contributions mitigate two failure modes in previous approaches: (1) spurious objects being generated without corresponding bounding boxes in the layout, and (2) overlapping bounding boxes in the layout leading to merged objects in images. Extensive quantitative evaluation and ablation studies demonstrate the impact of our contributions, with our model outperforming previous state-of-theart approaches on both the COCO-Stuff and Visual Genome datasets. Finally, we address an important limitation of evaluation metrics used in previous works by introducing Scene- FID – an object-centric adaptation of the popular Fréchet Inception Distance metric, that is better suited for multi-object images.

PDF Details

RLDM Conference 2019 Conference Abstract

Dead-ends and Secure Exploration in Reinforcement Learning

Mehdi Fatemi
Shikhar Sharma
Samira Ebrahimi Kahou

Many interesting applications of reinforcement learning (RL) involve MDPs that include many “dead-end” states. Upon reaching a dead-end state, the agent continues to interact with the environment in a dead-end trajectory before reaching a terminal state, but cannot collect any positive reward, regardless of whatever actions are chosen by the agent. The situation is even worse when existence of many dead- end states is coupled with distant positive rewards from any initial state (we call it Bridge Effect). Hence, conventional exploration techniques often incur prohibitively large training steps before convergence. To deal with the bridge effect, we propose a condition for exploration, called security. We next establish formal results that translate the security condition into the learning problem of an auxiliary value function. This new value function is used to cap “any” given exploration policy and is guaranteed to make it secure. As a special case, we use this theory and introduce secure random-walk. We next extend our results to the deep RL settings by identifying and addressing two main challenges that arise. Finally, we empirically compare secure random-walk with standard benchmarks in two sets of experiments including the Atari game of Montezuma’s Revenge.

PDF Details