Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Lecture

Non Conceptual Knowledge Systems

In course

DH-405: Foundations of digital humanities

This course introduces the core concepts and methodologies of Digital Humanities, integrating both theoretical and practical perspectives. Students learn to work with large-scale cultural datasets, acquiring the tools and techniques required for their proc ...

Description

This lecture covers the concept of non conceptual knowledge systems, focusing on image-to-image translation using conditional adversarial networks, contrastive learning for unpaired image-to-image translation, video-to-video synthesis, self-supervised learning, difficulties in self-supervised learning, video prediction models, universal non-conceptual representations, and the existence of non-conceptual knowledge demonstrated by various encoders/decoders converging in universal intermodal representation spaces.

Instructor

Frédéric Kaplan

Official source

Related concepts (31)

Feature learning

In machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process.

Unsupervised learning

Unsupervised learning, is paradigm in machine learning where, in contrast to supervised learning and semi-supervised learning, algorithms learn patterns exclusively from unlabeled data. Neural network tasks are often categorized as discriminative (recognition) or generative (imagination). Often but not always, discriminative tasks use supervised methods and generative tasks use unsupervised (see Venn diagram); however, the separation is very hazy. For example, object recognition favors supervised learning but unsupervised learning can also cluster objects into groups.

Self-supervised learning

Self-supervised learning (SSL) is a paradigm in machine learning for processing data of lower quality, rather than improving ultimate outcomes. Self-supervised learning more closely imitates the way humans learn to classify objects. The typical SSL method is based on an artificial neural network or other model such as a decision list. The model learns in two steps. First, the task is solved based on an auxiliary or pretext classification task using pseudo-labels which help to initialize the model parameters.

Text-to-video model

A text-to-video model is a machine learning model which takes as input a natural language description and produces a video matching that description. Video prediction on making objects realistic in a stable background is performed by using recurrent neural network for a sequence to sequence model with a connector convolutional neural network encoding and decoding each frame pixel by pixel, creating video using deep learning. Data collection and data set preparation using clear video from kinetic human action video.

Generative adversarial network

A generative adversarial network (GAN) is a class of machine learning framework and a prominent framework for approaching generative AI. The concept was initially developed by Ian Goodfellow and his colleagues in June 2014. In a GAN, two neural networks contest with each other in the form of a zero-sum game, where one agent's gain is another agent's loss. Given a training set, this technique learns to generate new data with the same statistics as the training set.

Related lectures (52)

Non-Conceptual Knowledge Systems: Style Transfer and Image Translation

Explores style transfer, image translation, self-supervised learning, video prediction, and image description generation using deep learning techniques.

Non conceptual knowledge systems

Explores the impact of Deep learning on Digital Humanities, focusing on non conceptual knowledge systems and recent advancements in AI.

Deep Learning Modus Operandi

Explores the benefits of deeper networks in deep learning and the importance of over-parameterization and generalization.

Introduction to Machine Learning

Provides an overview of Machine Learning, including historical context, key tasks, and real-world applications.

Generative Models: Self-Attention and Transformers