Search Results for author: Alan Sullivan

Found 10 papers, 1 papers with code

H-SAUR: Hypothesize, Simulate, Act, Update, and Repeat for Understanding Object Articulations from Interactions

no code implementations22 Oct 2022 Kei Ota, Hsiao-Yu Tung, Kevin A. Smith, Anoop Cherian, Tim K. Marks, Alan Sullivan, Asako Kanezaki, Joshua B. Tenenbaum

The world is filled with articulated objects that are difficult to determine how to use from vision alone, e. g., a door might open inwards or outwards.

InSeGAN: A Generative Approach to Segmenting Identical Instances in Depth Images

no code implementations ICCV 2021 Anoop Cherian, Goncalo Dias Pais, Siddarth Jain, Tim K. Marks, Alan Sullivan

To use our model for instance segmentation, we propose an instance pose encoder that learns to take in a generated depth image and reproduce the pose code vectors for all of the object instances.

Generative Adversarial Network Instance Segmentation +2

Towards Learning Affine-Invariant Representations via Data-Efficient CNNs

no code implementations31 Aug 2019 Xenju Xu, Guanghui Wang, Alan Sullivan, Ziming Zhang

In this paper we propose integrating a priori knowledge into both design and training of convolutional neural networks (CNNs) to learn object representations that are invariant to affine transformations (i. e., translation, scale, rotation).

Translation

Verification of Very Low-Resolution Faces Using An Identity-Preserving Deep Face Super-Resolution Network

no code implementations26 Mar 2019 Esra Ataer-Cansizoglu, Michael Jones, Ziming Zhang, Alan Sullivan

Face super-resolution methods usually aim at producing visually appealing results rather than preserving distinctive features for further face identification.

Face Identification Face Recognition +2

Equilibrated Recurrent Neural Network: Neuronal Time-Delayed Self-Feedback Improves Accuracy and Stability

no code implementations2 Mar 2019 Ziming Zhang, Anil Kag, Alan Sullivan, Venkatesh Saligrama

We show that such self-feedback helps stabilize the hidden state transitions leading to fast convergence during training while efficiently learning discriminative latent features that result in state-of-the-art results on several benchmark datasets at test-time.

Time-Delay Momentum: A Regularization Perspective on the Convergence and Generalization of Stochastic Momentum for Deep Learning

no code implementations2 Mar 2019 Ziming Zhang, Wenju Xu, Alan Sullivan

In this paper we study the problem of convergence and generalization error bound of stochastic momentum for deep learning from the perspective of regularization.

Sim-to-Real Transfer Learning using Robustified Controllers in Robotic Tasks involving Complex Dynamics

no code implementations13 Sep 2018 Jeroen van Baar, Alan Sullivan, Radu Cordorel, Devesh Jha, Diego Romeres, Daniel Nikovski

Another advantage when robots are involved, is that the amount of time a robot is occupied learning a task---rather than being productive---can be reduced by transferring the learned task to the real robot.

Friction Transfer Learning

Sem-GAN: Semantically-Consistent Image-to-Image Translation

1 code implementation12 Jul 2018 Anoop Cherian, Alan Sullivan

To this end, we present a semantically-consistent GAN framework, dubbed Sem-GAN, in which the semantics are defined by the class identities of image segments in the source domain as produced by a semantic segmentation algorithm.

Image Segmentation Image-to-Image Translation +3

Deformable Part Networks

no code implementations22 May 2018 Ziming Zhang, Rongmei Lin, Alan Sullivan

In this paper we propose novel Deformable Part Networks (DPNs) to learn {\em pose-invariant} representations for 2D object recognition.

Object Recognition

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