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Found 13 papers, 4 papers with code
A Comprehensive Literature Review on Sweet Orange Leaf Diseases
This comprehensive literature review is systematically based on leaf disease and machine learning methodologies applied to the detection of damaged leaves via image classification.
Machine Learning-Based Tea Leaf Disease Detection: A Comprehensive Review
This paper delivers a systematic review of the literature on machine learning methodologies applied to diagnose tea leaf disease via image classification.
Domain-specific optimization and diverse evaluation of self-supervised models for histopathology
Foundation models in histopathology that learn general representations across a wide range of tissue types, diagnoses, and magnifications offer the potential to reduce the data, compute, and technical expertise necessary to develop task-specific deep learning models with the required level of model performance.
Systematic generalisation with group invariant predictions
We consider situations where the presence of dominant simpler correlations with the target variable in a training set can cause an SGD-trained neural network to be less reliant on more persistently-correlating complex features.
Integrating Categorical Semantics into Unsupervised Domain Translation
While unsupervised domain translation (UDT) has seen a lot of success recently, we argue that mediating its translation via categorical semantic features could broaden its applicability.
Virtual Experience to Real World Application: Sidewalk Obstacle Avoidance Using Reinforcement Learning for Visually Impaired
We trained a Sidewalk Obstacle Avoidance Agent (SOAA) through reinforcement learning in a simulated robotic environment.
Detecting semantic anomalies
We critically appraise the recent interest in out-of-distribution (OOD) detection and question the practical relevance of existing benchmarks.
On Difficulties of Probability Distillation
Probability distillation has recently been of interest to deep learning practitioners as it presents a practical solution for sampling from autoregressive models for deployment in real-time applications.
Improved Training of Wasserstein GANs
Generative Adversarial Networks (GANs) are powerful generative models, but suffer from training instability.
Ranked #3 on
Image Generation
on CAT 256x256
PixelVAE: A Latent Variable Model for Natural Images
Natural image modeling is a landmark challenge of unsupervised learning.
Optimizing Expected Intersection-Over-Union With Candidate-Constrained CRFs
Currently, there are two dominant approaches: the first approximates the Expected-IoU (EIoU) score as Expected-Intersection-over-Expected-Union (EIoEU); and the second approach is to compute exact EIoU but only over a small set of high-quality candidate solutions.
Reducing Overfitting in Deep Networks by Decorrelating Representations
One major challenge in training Deep Neural Networks is preventing overfitting.
Candidate Constrained CRFs for Loss-Aware Structured Prediction
The result is that we can use loss-aware prediction methodology to improve performance of the highly tuned pipeline system.
