Scaling laws of synthetic images for model training... for now
Recent significant advances in text-to-image models unlock the possibility of training vision
systems using synthetic images potentially overcoming the difficulty of collecting curated …
systems using synthetic images potentially overcoming the difficulty of collecting curated …
[HTML][HTML] Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions
Understanding black box models has become paramount as systems based on opaque
Artificial Intelligence (AI) continue to flourish in diverse real-world applications. In response …
Artificial Intelligence (AI) continue to flourish in diverse real-world applications. In response …
TP2O: Creative Text Pair-to-Object Generation Using Balance Swap-Sampling
Generating creative combinatorial objects from two seemingly unrelated object texts is a
challenging task in text-to-image synthesis, often hindered by a focus on emulating existing …
challenging task in text-to-image synthesis, often hindered by a focus on emulating existing …
Unlocking feature visualization for deep network with magnitude constrained optimization
Feature visualization has gained significant popularity as an explainability method,
particularly after the influential work by Olah et al. in 2017. Despite its success, its …
particularly after the influential work by Olah et al. in 2017. Despite its success, its …
Unlocking feature visualization for deeper networks with magnitude constrained optimization
Feature visualization has gained substantial popularity, particularly after the influential work
by Olah et al. in 2017, which established it as a crucial tool for explainability. However, its …
by Olah et al. in 2017, which established it as a crucial tool for explainability. However, its …
Latent Representation Matters: Human-like Sketches in One-shot Drawing Tasks
Humans can effortlessly draw new categories from a single exemplar, a feat that has long
posed a challenge for generative models. However, this gap has started to close with recent …
posed a challenge for generative models. However, this gap has started to close with recent …
Dual Thinking and Perceptual Analysis of Deep Learning Models using Human Adversarial Examples
K Dayanandan, A Sinha, B Lall - arXiv preprint arXiv:2406.06967, 2024 - arxiv.org
The dual thinking framework considers fast, intuitive processing and slower, logical
processing. The perception of dual thinking in vision requires images where inferences from …
processing. The perception of dual thinking in vision requires images where inferences from …
Visual Representation Learning from Synthetic Data
L Fan - 2024 - dspace.mit.edu
Representation learning is crucial for developing robust vision systems. The effectiveness of
this learning process largely depends on the quality and quantity of data. Synthetic data …
this learning process largely depends on the quality and quantity of data. Synthetic data …
AI and Art
C Moruzzi, OL Campagner - … Co-Creation and Creativity: The New …, 2024 - books.google.com
Since the beginning of the twenty-first century, the accelerated advancements in artificial
intelligence (AI) technology, coupled with its increased use in the creative industry, have …
intelligence (AI) technology, coupled with its increased use in the creative industry, have …
Unlocking Feature Visualization for Deep Network with MAgnitude Constrained Optimization
Feature visualization has gained significant popularity as an explainability method,
particularly after the influential work by Olah et al. in 2017. Despite its success, its …
particularly after the influential work by Olah et al. in 2017. Despite its success, its …