Autoencoder Patch Clustering for Unlabeled Object Discovery
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manual labeling of objects in images for supervised learning is inefficient due to high human error rates and requires specific object type annotation, limiting the applicability and accuracy of object detection models.
Innovation Solution
An AI machine uses a memory data structure and unsupervised learning to discover objects by clustering in pixel and latent spaces, employing a modulated contrastive loss to learn pattern embeddings and distances, enabling the detection of frequent objects without manual labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual labeling is used for training object detection models, then the model can be trained with human-provided labels, but the process requires high human effort and is prone to errors
Solution Approach 1:
The system enables automatic self-labeling by using an autoencoder to cluster image patches and generate labels without human intervention. The autoencoder learns to compress and reconstruct image patches, using reconstruction error to identify and label objects automatically, replacing the manual labeling process
Solution Approach 2:
The patent replaces the mechanical manual labeling process with an automated AI-based system. The autoencoder algorithm automatically processes images, clusters patches, and generates labels, substituting human labor with a machine learning-based automated system
2Measurement precision
If manual labeling is used, then specific object types can be annotated, but the process is limited to known object types and cannot discover new objects
Solution Approach 1:
The system performs unsupervised learning where the autoencoder automatically discovers object patterns and creates labels without pre-defined categories. The clustering algorithm organizes patches based on their visual characteristics, enabling the system to identify and label object types that were not explicitly taught during training
Solution Approach 2:
Instead of teaching the system to recognize predefined object categories through manual labeling, the patent inverts the approach by having the system automatically discover and create its own object representations. The autoencoder reconstructs patches and uses reconstruction error to identify object patterns, inverting the traditional supervised learning paradigm
3Reliability
If manual labeling is used, then training data can be prepared, but human error rates are orders of magnitude higher than machine-based processes
Solution Approach 1:
The patent replaces the human-based labeling system with an automated autoencoder-based system. The autoencoder uses neural network computations to process images, cluster patches, and generate labels, substituting human judgment with a machine learning algorithm that operates consistently without human error
Solution Approach 2:
The system incorporates feedback through the autoencoder's reconstruction process. The autoencoder continuously adjusts its internal representations based on reconstruction error, using this feedback to refine its clustering and labeling decisions, thereby improving label accuracy through iterative self-correction
Data Source
AI summary
A problem of supervised learning is overcome by using patches to discover objects in unlabeled training images. The discovered objects are embedded in a pattern space. An AI machine replaces manual entry steps of training with a machine-centric process including clustering in a pixel space, clustering in latent space and building the pattern space based on different losses derived from pixel space clustering and latent space clustering. A distance structure in the pattern space captures the co-occurrence of patterns due to frequently appearing objects in training image data. Embodiments provide image representation based on local image patch naturally handles the position and scale invariance property that is important to effective object detection. Embodiments successfully identifies frequent objects such as human faces, human bodies, animals, or vehicles from unorganized data images based on a small quantity of training images.