School of Artificial Intelligence at the Nanjing University (NJU)School of Artificial Intelligence at the Nanjing University (NJU)

Overview

F-Principle

This is an exercise problem of the digital signal processing (DSP) course at School of Artificial Intelligence at the Nanjing University (NJU), teaching by Han-Jia Ye. The course homepage is at DSP. This exercise is written by Jia-Qi Yang. Please feel free to contact me by mailing [email protected] if you have any questions.

Problem 1: Understanding F-Principle (35pt)

Read following articles:

  1. F-Principle
  2. Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks

Then, answer following questions:

  1. What is F-Principle ? (5pt)
  2. Why F-Principle is important ? (5pt)
  3. What are the differences between response frequency and input frequency ? Which one is used in F-Principle ? (5pt)
  4. How is frequency defined in high-dimensional functions ? Why ? (10pt)
  5. How does the authors verify F-Principle experimentally ? (10pt)

Problem 2: Reproducing F-Principle (65pt)

Code to reproduce F-Principle by the authors is published at F-Principle Github.

You may modify F-Principle Github to conduct following experiments. However, this implementation is based on tf1.x, and the high-dim experiments are not implemented. You may also choose to extend pytorch training scripts provided in src/.

2.1 Low-dim Experiment (25pt)

Read F-Principle in low-dim experiments.

  1. Plot training procedure in Spatial Domain, i.e. the first figure in F-Principle in low-dim experiments. (10pt)
  2. Plot training procedure in Fourier Domain, i.e. the second figure in F-Principle in low-dim experiments. (10pt)

You may plot several figures instead of gifs in F-Principle in low-dim experiments.

2.2 High-dim Experiment (30pt)

Read F-Principle in high-dim experiments.

  1. Implement the projection method or the filtering method on MNIST dataset.
  2. Describe the procedure of your method using pseudo-code.
  3. Inspect how each response frequency component (e.g. high-frequency and low-frequency) converges. You may plot figures or using tables to demostrate your results.

2.3 Summay (10pt)

  1. What did you learn from this practice problem ? (5pt)
  2. What problems did you encounter and how did you solve them ? (5pt)
Owner
Thyrix
Thyrix
The repository for our EMNLP 2021 paper "Finnish Dialect Identification: The Effect of Audio and Text"

Finnish Dialect Identification The repository for our EMNLP 2021 paper "Finnish Dialect Identification: The Effect of Audio and Text". We present a te

Rootroo Ltd 2 Dec 25, 2021
Attention for PyTorch with Linear Memory Footprint

Attention for PyTorch with Linear Memory Footprint Unofficially implements https://arxiv.org/abs/2112.05682 to get Linear Memory Cost on Attention (+

11 Jan 09, 2022
RL-driven agent playing tic-tac-toe on starknet against challengers.

tictactoe-on-starknet RL-driven agent playing tic-tac-toe on starknet against challengers. GUI reference: https://pythonguides.com/create-a-game-using

21 Jul 30, 2022
Image Processing, Image Smoothing, Edge Detection and Transforms

opevcvdl-hw1 This project uses openCV and Qt to achieve the requirements. Version Python 3.7 opencv-contrib-python 3.4.2.17 Matplotlib 3.1.1 pyqt5 5.1

Kenny Cheng 3 Aug 17, 2022
yufan 81 Dec 08, 2022
Code for paper "Multi-level Disentanglement Graph Neural Network"

Multi-level Disentanglement Graph Neural Network (MD-GNN) This is a PyTorch implementation of the MD-GNN, and the code includes the following modules:

Lirong Wu 6 Dec 29, 2022
World Models with TensorFlow 2

World Models This repo reproduces the original implementation of World Models. This implementation uses TensorFlow 2.2. Docker The easiest way to hand

Zac Wellmer 234 Nov 30, 2022
Official implementation of the article "Unsupervised JPEG Domain Adaptation For Practical Digital Forensics"

Unsupervised JPEG Domain Adaptation for Practical Digital Image Forensics @WIFS2021 (Montpellier, France) Rony Abecidan, Vincent Itier, Jeremie Boulan

Rony Abecidan 6 Jan 06, 2023
Context Axial Reverse Attention Network for Small Medical Objects Segmentation

CaraNet: Context Axial Reverse Attention Network for Small Medical Objects Segmentation This repository contains the implementation of a novel attenti

401 Dec 23, 2022
In this project we combine techniques from neural voice cloning and musical instrument synthesis to achieve good results from as little as 16 seconds of target data.

Neural Instrument Cloning In this project we combine techniques from neural voice cloning and musical instrument synthesis to achieve good results fro

Erland 127 Dec 23, 2022
Repo for EchoVPR: Echo State Networks for Visual Place Recognition

EchoVPR Repo for EchoVPR: Echo State Networks for Visual Place Recognition Currently under development Dirs: data: pre-collected hidden representation

Anil Ozdemir 4 Oct 04, 2022
[TPAMI 2021] iOD: Incremental Object Detection via Meta-Learning

Incremental Object Detection via Meta-Learning To appear in an upcoming issue of the IEEE Transactions on Pattern Analysis and Machine Intelligence (T

Joseph K J 66 Jan 04, 2023
QTool: A Low-bit Quantization Toolbox for Deep Neural Networks in Computer Vision

This project provides abundant choices of quantization strategies (such as the quantization algorithms, training schedules and empirical tricks) for quantizing the deep neural networks into low-bit c

Monash Green AI Lab 51 Dec 10, 2022
System Design course at HSE (2021)

System Design course at HSE (2021) Wiki-страница курса Структура репозитория: slides - директория с презентациями с занятий tasks - материалы для выпо

22 Dec 25, 2022
Retinal Vessel Segmentation with Pixel-wise Adaptive Filters (ISBI 2022)

Official code of Retinal Vessel Segmentation with Pixel-wise Adaptive Filters and Consistency Training (ISBI 2022)

anonymous 14 Oct 27, 2022
[CVPR 2021] VirTex: Learning Visual Representations from Textual Annotations

VirTex: Learning Visual Representations from Textual Annotations Karan Desai and Justin Johnson University of Michigan CVPR 2021 arxiv.org/abs/2006.06

Karan Desai 533 Dec 24, 2022
NLU Dataset Diagnostics

NLU Dataset Diagnostics This repository contains data and scripts to reproduce the results from our paper: Aarne Talman, Marianna Apidianaki, Stergios

Language Technology at the University of Helsinki 1 Jul 20, 2022
Official pytorch code for "APP: Anytime Progressive Pruning"

APP: Anytime Progressive Pruning Diganta Misra1,2,3, Bharat Runwal2,4, Tianlong Chen5, Zhangyang Wang5, Irina Rish1,3 1 Mila - Quebec AI Institute,2 L

Landskape AI 12 Nov 22, 2022
Deal or No Deal? End-to-End Learning for Negotiation Dialogues

Introduction This is a PyTorch implementation of the following research papers: (1) Hierarchical Text Generation and Planning for Strategic Dialogue (

Facebook Research 1.4k Dec 29, 2022
Consistency Regularization for Adversarial Robustness

Consistency Regularization for Adversarial Robustness Official PyTorch implementation of Consistency Regularization for Adversarial Robustness by Jiho

40 Dec 17, 2022