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
Submodular Subset Selection for Active Domain Adaptation (ICCV 2021)

S3VAADA: Submodular Subset Selection for Virtual Adversarial Active Domain Adaptation ICCV 2021 Harsh Rangwani, Arihant Jain*, Sumukh K Aithal*, R. Ve

Video Analytics Lab -- IISc 13 Dec 28, 2022
Code & Models for Temporal Segment Networks (TSN) in ECCV 2016

Temporal Segment Networks (TSN) We have released MMAction, a full-fledged action understanding toolbox based on PyTorch. It includes implementation fo

1.4k Jan 01, 2023
Wordle-solver - Wordle answer generation program in python

🟨 Wordle Solver 🟩 Wordle answer generation program in python ✔️ Requirements U

Dahyun Kang 4 May 28, 2022
Image super-resolution through deep learning

srez Image super-resolution through deep learning. This project uses deep learning to upscale 16x16 images by a 4x factor. The resulting 64x64 images

David Garcia 5.3k Dec 28, 2022
One implementation of the paper "DMRST: A Joint Framework for Document-Level Multilingual RST Discourse Segmentation and Parsing".

Introduction One implementation of the paper "DMRST: A Joint Framework for Document-Level Multilingual RST Discourse Segmentation and Parsing". Users

seq-to-mind 18 Dec 11, 2022
Image-to-Image Translation in PyTorch

CycleGAN and pix2pix in PyTorch New: Please check out contrastive-unpaired-translation (CUT), our new unpaired image-to-image translation model that e

Jun-Yan Zhu 19k Jan 07, 2023
Procedural 3D data generation pipeline for architecture

Synthetic Dataset Generator Authors: Stanislava Fedorova Alberto Tono Meher Shashwat Nigam Jiayao Zhang Amirhossein Ahmadnia Cecilia bolognesi Dominik

Computational Design Institute 49 Nov 25, 2022
Automatic caption evaluation metric based on typicality analysis.

SeMantic and linguistic UndeRstanding Fusion (SMURF) Automatic caption evaluation metric described in the paper "SMURF: SeMantic and linguistic UndeRs

Joshua Feinglass 6 Jan 09, 2022
Raptor-Multi-Tool - Raptor Multi Tool With Python

Promises 🔥 20 Stars and I'll fix every error that there is 50 Stars and we will

Aran 44 Jan 04, 2023
Code for reproducing key results in the paper "InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets"

Status: Archive (code is provided as-is, no updates expected) InfoGAN Code for reproducing key results in the paper InfoGAN: Interpretable Representat

OpenAI 1k Dec 19, 2022
A machine learning project which can detect and predict the skin disease through image recognition.

ML-Project-2021 A machine learning project which can detect and predict the skin disease through image recognition. The dataset used for this is the H

Debshishu Ghosh 1 Jan 13, 2022
Title: Graduate-Admissions-Predictor

The purpose of this project is create a predictive model capable of identifying the probability of a person securing an admit based on their personal profile parameters. Simplified visualisations hav

Akarsh Singh 1 Jan 26, 2022
The aim of the game, as in the original one, is to find a specific image from a group of different images of a person's face

GUESS WHO Main Links: [Github] [App] Related Links: [CLIP] [Celeba] The aim of the game, as in the original one, is to find a specific image from a gr

Arnau - DIMAI 3 Jan 04, 2022
Official PyTorch implementation of Learning Intra-Batch Connections for Deep Metric Learning (ICML 2021) published at International Conference on Machine Learning

About This repository the official PyTorch implementation of Learning Intra-Batch Connections for Deep Metric Learning. The config files contain the s

Dynamic Vision and Learning Group 41 Dec 10, 2022
This is the source code for: Context-aware Entity Typing in Knowledge Graphs.

This is the source code for: Context-aware Entity Typing in Knowledge Graphs.

9 Sep 01, 2022
Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions

torch-imle Concise and self-contained PyTorch library implementing the I-MLE gradient estimator proposed in our NeurIPS 2021 paper Implicit MLE: Backp

UCL Natural Language Processing 249 Jan 03, 2023
A wrapper around SageMaker ML Lineage Tracking extending ML Lineage to end-to-end ML lifecycles, including additional capabilities around Feature Store groups, queries, and other relevant artifacts.

ML Lineage Helper This library is a wrapper around the SageMaker SDK to support ease of lineage tracking across the ML lifecycle. Lineage artifacts in

AWS Samples 12 Nov 01, 2022
Implementation for Stankevičiūtė et al. "Conformal time-series forecasting", NeurIPS 2021.

Conformal time-series forecasting Implementation for Stankevičiūtė et al. "Conformal time-series forecasting", NeurIPS 2021. If you use our code in yo

Kamilė Stankevičiūtė 36 Nov 21, 2022
The fastest way to visualize GradCAM with your Keras models.

VizGradCAM VizGradCam is the fastest way to visualize GradCAM in Keras models. GradCAM helps with providing visual explainability of trained models an

58 Nov 19, 2022
Making a music video with Wav2CLIP and VQGAN-CLIP

music2video Overview A repo for making a music video with Wav2CLIP and VQGAN-CLIP. The base code was derived from VQGAN-CLIP The CLIP embedding for au

Joel Jang | 장요엘 163 Dec 26, 2022