Abhishek Sinha

I am a second year graduate student in the department of Computer Science at Stanford University. I am interested in the domain of computer vision and deep learning. I am specifically interested in topics such as generative models, anomaly detection, self-supervised learning and active learning.

I am currently a Research Assistant under Professor Stefano Ermon and am pursuing research in generative models. Previously I was a Course Assistant for the couse CS 330 - "Deep Multi Task and Meta Learning" taught by Professor Chelsea Finn.

Prior to coming here, I was working at Adobe India as Member of Technical Staff-2. I worked on a deep learning based visual search product for clothing based recommendation which accepts images, segments them and then recommends related desired products. This work won the "Best Paper Award" at a CVPR workshop, 2019. I was also involved in several other research based projects during my work. Prior to my work, I did my undergraduation from Indian Institute of Tenchnology Kharagpur with a major in Electronics and Electrical Communication Engineering and a minor in Computer Science.

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Selected Publications

Introspection: Accelerating Neural Network Training By Learning Weight Evolution

Developed an algorithm to speed up training of deep neural networks by predicting future weight values.

Achieved 20% and 40% improvement in training time for Cifar-10 and ImageNet datasets respectively.

The work was accepted at ICLR, 2017.


Charting the Right Manifold: Manifold Mixup for Few-shot Learning

Used self-supervision techniques - rotation and exemplar, followed by manifold mixup for few-shot classification tasks.

The proposed approach beats the current state-of-the-art accuracy on mini-ImageNet, CUB and CIFAR-FS datasets by 3-8%.

Work accepted at WACV, 2020.


Harnessing the Vulnerability of Latent Layers in Adversarially Trained Models

Analyzed the adversarial trained models for vulnerability against adversarial perturbations at the latent layers.

The algorithm achieved the state-of-the art adversarial accuracy against strong adversarial attacks.

Work accepted at IJCAI, 2019.


Attention Based Natural Language Grounding By Navigating Virtual Environment

Made a 2D grid environment in which an agent performs tasks on the basis of natural language sentence. Developed a new fusion mechanism for the fusion of visual and textual features to solve the problem.

The proposed methodology outperformed the state-of-the-art in terms of both speed and performance for the 2D as well as a 3D environment.

Work accepted at WACV, 2019.


Powering Robust Fashion Retrieval with Information Rich Feature Embeddings

Proposed a grid based training of siamese networks, allowing the network to observe mutiplte positive and negative image instances simultaneously.

Best Paper Award at CVPR Workshop, 2019.


Improving Classification Performance of Support VectorMachines via Guided Custom Kernel Search

Used a modification of the neural architecture search to discover a kernel function for SVM over MNIST dataset.

Work accepted at GECCO, 2019.

Selected Projects

Face analyzer tool

Built a face analyzer tool utilizing deep learning techniques to provide users with an unbiased analysis of their facial appearance.

The project was the winner of the Microsoft AI Hackathon competition held at IIT Kharagpur.


Autonomous snake game using DQN

Implemented Deep Q learning for the snake game. Built the game in pygame which could be then controlled by a deep learning agent.

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