Resource of free step by step video how to guides to get you started with machine learning.
Wednesday, June 3, 2020
Learning To Classify Images Without Labels (Paper Explained)
How do you learn labels without labels? How do you classify images when you don't know what to classify them into? This paper investigates a new combination of representation learning, clustering, and self-labeling in order to group visually similar images together - and achieves surprisingly high accuracy on benchmark datasets. OUTLINE: 0:00 - Intro & High-level Overview 2:15 - Problem Statement 4:50 - Why naive Clustering does not work 9:25 - Representation Learning 13:40 - Nearest-neighbor-based Clustering 28:00 - Self-Labeling 32:10 - Experiments 38:20 - ImageNet Experiments 41:00 - Overclustering Paper: https://ift.tt/3eQqHCd Code: https://ift.tt/2MpCJWP Abstract: Is it possible to automatically classify images without the use of ground-truth annotations? Or when even the classes themselves, are not a priori known? These remain important, and open questions in computer vision. Several approaches have tried to tackle this problem in an end-to-end fashion. In this paper, we deviate from recent works, and advocate a two-step approach where feature learning and clustering are decoupled. First, a self-supervised task from representation learning is employed to obtain semantically meaningful features. Second, we use the obtained features as a prior in a learnable clustering approach. In doing so, we remove the ability for cluster learning to depend on low-level features, which is present in current end-to-end learning approaches. Experimental evaluation shows that we outperform state-of-the-art methods by huge margins, in particular +26.9% on CIFAR10, +21.5% on CIFAR100-20 and +11.7% on STL10 in terms of classification accuracy. Furthermore, results on ImageNet show that our approach is the first to scale well up to 200 randomly selected classes, obtaining 69.3% top-1 and 85.5% top-5 accuracy, and marking a difference of less than 7.5% with fully-supervised methods. Finally, we applied our approach to all 1000 classes on ImageNet, and found the results to be very encouraging. The code will be made publicly available. Authors: Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, Marc Proesmans, Luc Van Gool Links: YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher BitChute: https://ift.tt/38iX6OV Minds: https://ift.tt/37igBpB
Subscribe to:
Post Comments (Atom)
-
JavaやC++で作成された具体的なルールに従って動く従来のプログラムと違い、機械学習はデータからルール自体を推測するシステムです。機械学習は具体的にどのようなコードで構成されているでしょうか? 機械学習ゼロからヒーローへの第一部ではそのような疑問に応えるため、ガイドのチャー...
-
#deeplearning #noether #symmetries This video includes an interview with first author Ferran Alet! Encoding inductive biases has been a lo...
-
Using GPUs in TensorFlow, TensorBoard in notebooks, finding new datasets, & more! (#AskTensorFlow) [Collection] In a special live ep...
-
How to Do PS2 Filter (Tiktok PS2 Filter Tutorial), AI tiktok filter Create your own PS2 Filter photos with this simple guide! 🎮📸 Please...
-
Challenge scenario You were recently hired as a Machine Learning Engineer at a startup movie review website. Your manager has tasked you wit...
-
#ai #attention #transformer #deeplearning Transformers are famous for two things: Their superior performance and their insane requirements...
-
Visual scenes are often comprised of sets of independent objects. Yet, current vision models make no assumptions about the nature of the p...
-
Hello Friends, In this episode we will explore AI tool Craiyan which helps us to create images just by providing the text information. ht...
-
Why are humans so good at video games? Maybe it's because a lot of games are designed with humans in mind. What happens if we change t...
-
#alibi #transformers #attention Transformers are essentially set models that need additional inputs to make sense of sequence data. The mo...
No comments:
Post a Comment