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Found 9 papers, 1 papers with code
Learn and Search: An Elegant Technique for Object Lookup using Contrastive Learning
The rapid proliferation of digital content and the ever-growing need for precise object recognition and segmentation have driven the advancement of cutting-edge techniques in the field of object classification and segmentation.
Unsupervised learning based object detection using Contrastive Learning
Training image-based object detectors presents formidable challenges, as it entails not only the complexities of object detection but also the added intricacies of precisely localizing objects within potentially diverse and noisy environments.
Productive Crop Field Detection: A New Dataset and Deep Learning Benchmark Results
In precision agriculture, detecting productive crop fields is an essential practice that allows the farmer to evaluate operating performance separately and compare different seed varieties, pesticides, and fertilizers.
CLAWS: Contrastive Learning with hard Attention and Weak Supervision
Learning effective visual representations without human supervision is a long-standing problem in computer vision.
Cluster Analysis with Deep Embeddings and Contrastive Learning
Unsupervised disentangled representation learning is a long-standing problem in computer vision.
Generalizable semi-supervised learning method to estimate mass from sparsely annotated images
The deep neural network is initially trained to predict mass on laboratory data (bamboo) and then transfer learning is utilized to apply the same methods to estimate mass of sugarcane.
Granular Learning with Deep Generative Models using Highly Contaminated Data
An approach to utilize recent advances in deep generative models for anomaly detection in a granular (continuous) sense on a real-world image dataset with quality issues is detailed using recent normalizing flow models, with implications in many other applications/domains/data types.
Deep Generative Models Strike Back! Improving Understanding and Evaluation in Light of Unmet Expectations for OoD Data
Finally, a look to the previous generation of generative models in the form of probabilistic principal component analysis is inspired, and revisited for the same data-sets and shown to work really well for discriminating anomalies based on likelihood in a fully unsupervised fashion compared with pixelCNN++, GLOW, and real NVP with less complexity and faster training.
Mass Estimation from Images using Deep Neural Network and Sparse Ground Truth
Since the number of images for any given run are too large to fit on typical GPU vRAM, an implementation is shown that compensates for the limited memory but still achieve fast training times.
