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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About Me
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Posts
Motivating Interpretability - A Researcher and Engineering Perspective
Published:
Deep neural networks (DNNs) are known to be almost fully opaque compared to traditional algorithms, even to top engineers and scientists who use DNNs. Many reasons have been proposed for why understanding the decision making process of these models is important, such as human curiosity, scientific discovery, bias detection, or algorithm safety measures and auditing. Furthermore, a model which is interpretable may exhibit more fairness, reliability, and trust to the general public 1. Several methods to interpret DNNs in computer vision have been proposed to varying degrees of success. For example, many recent SOTA saliency map visualization methods have been shown to be on par with some random baselines. Similar issues with other interpretability methods have been criticized on social media, pointing out that the reliance on these methods in mission critical situations will likely do more harm than good.
Understanding Mutual Information
Published:
For this post I hope to accomplish a few different things. I will review a brilliant document put together by Erik G. Learned-Miller. I found this document when attempting to better understand the concept of ‘Mutual Information’, and it has been by far the most influential document in my understanding of entropy and mutual information. It is a short document, only 3 pages, with the purpose of being an introduction to entropy and mutual information for discrete random variables. Erik does something that I think so many teachers miss when introducing students to new concepts, which is using real-world, easy to understand examples to aid with the formulas. I hope I can add some intuition behind what entropy, joint entropy, and mutual information actually represent, as well as review some simple and more complex examples that I am currently working on in my research.
publications
Feature Binding with Category-Dependant MixUp for Semantic Segmentation and Adversarial Robustness
Published in BMVC (Oral), 2020
We propose a new type of data augmentation for dense labelling tasks. We train neural networks to seperate and label mixed images based on their co-occurance probabilities.
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Shape or Texture: Understanding Discriminative Features in CNNs
Published in ICLR, 2021
We present two approaches for quantifying the shape and texture encoded in convolutional neural networks.
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SegMix: Co-occurrence Driven Mixup for Semantic Segmentation and Adversarial Robustness
Published in International Journal of Computer Vision, 2021
We present a strategy for training convolutional neural networks for the task of dense image labelling by blending images based on (i) categorical clustering or (ii) the co-occurrence likelihood of categories.
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Global Pooling, More than Meets the Eye: Position Information is Encoded Channel-Wise in CNNs
Published in ICCV, 2021
In this paper, we challenge the common assumption that collapsing the spatial dimensions of a 3D (spatial-channel) tensor in a convolutional neural network (CNN) into a vector via global pooling removes all spatial information. Specifically, we demonstrate that positional information is encoded based on the ordering of the channel dimensions, while semantic information is largely not.
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Simpler Does It: Generating Semantic Labels with Objectness Guidance
Published in BMVC, 2021
We design a novel, efficient, and robust weakly-supervised method for generating semantic segmentation pseudo-labels from CAMs or bounding boxes.
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A Deeper Dive Into What Deep Spatiotemporal Networks Encode: Quantifying Static vs. Dynamic Information
Published in CVPR, 2022
We quantify and explore the static and dynamic information contained in deep spatiotemporal networks.
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Quantifying and Learning Static vs. Dynamic Information in Deep Spatiotemporal Networks
Published in TPAMI, 2022
We quantify, and design algorithms to learn, static and dynamic information in deep spatiotemporal networks.
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Position, Padding and Predictions: A Deeper Look at Position Information in CNNs
Published in International Journal of Computer Vision, 2024
We explore the existance of absolute position information in CNNs in relation to their padding type or other border heuristics.
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Understanding Video Transformers via Universal Concept Discovery
Published in CVPR (Highlight), 2024
We design a method for discovering universal spatiotemporal concepts in deep video transformers.
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Visual Concept Connectome (VCC): Open World Concept Discovery and their Interlayer Connections in Deep Models
Published in CVPR (Highlight), 2024
We discover and quantify concept connections in multi-layer deep models.
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.