technical

Understanding Usage of plt, figure, subplot, axes, axis in matplotlib

When working with python libraries, especially for visualization, I usually get confused my number of options available for plotting. Example: 1. plt.plot() 2. ax = plt.subplot() ax.plot(x, y) 3. fig1, ((ax1, ax2), (ax3, ax4)) = plt.

CLIP: Connecting Text and Images

Paper: Learning Transferable Visual Models From Natural Language Supervision PDF: Learning Transferable Visual Models From Natural Language Supervision Blog: CLIP: Connecting Text and Images General Terms:

Datasets for Fine-Grained Image Classification

iNat2017 Data: https://github.com/visipedia/inat_comp/tree/master/2017 iNat2018 and iNat2019 Data: https://github.com/visipedia/inat_comp/blob/master/2018/README.md Data: https://github.com/visipedia/inat_comp Details: The dataset is similar to iNat2017 with small differences, which are mentioned in the website.

Hyperopt: A tool for parameter tuning

In deep learning, it is not easy to tune hyperparameters for optimal results. If we have 2 parameters (each with 3 prior desirable values), it is an easier problem. We will have possible combinations to try.

KL-Divergence

What is KL-Divergence KL Divergence is a measure of how one probabilty distributon is different from another. Some people also call it the distance between two distributions, however, strictly speaking it is not the distance.

Low/Few Shot Learning

Question: If a class has only two samples, can a computer make correct prediction? Note: Number of samples is too less for training. Approach: Few Shot Learning Few shot learning is a problem where we try to learn when the training data is very small.

Presence-Only Geographical Priors for Fine-Grained Image Classification

Paper: Link Objective: Explores how can we use additional meta-data available to make better classification (in this case animal species). Explores how to make best use of additional meta data which comes with most images today.

The iNaturalist Species Classification and Detection Dataset

Paper: Link Objective: Focuses on species of plants and animals captured in wide variety of situations, different camera types, varying image quality, feature large class imbalance and verified by citizen scientists.

The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition

Paper: Link Objective: Leverage free, noisy data from the web to train effective models of fine-grained recognition. Summary: Interesting paper on using noisy data from the web. They sample images directly from Google search, using all returned images as images for a given category.

We Have So Much In Common: Modeling Semantic Relational Set Abstractions In Videos

General Terms: Semantic: Semantics is the study of relationship between words and how we can draw meaning from words. Example, A child could be called a child, kid, boy, girl, son, daughter.