The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection. The publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld. ILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”. The ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided.
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Color Image Denoising
Few-Shot Learning
Out-of-Distribution Detection
Unsupervised Domain Adaptation
Long-tail Learning
Domain Generalization
Binarization
Density Estimation
Image Recognition
Generalized Zero-Shot Learning
Stochastic Optimization
Image Compression
Quantization
Small Data Image Classification
Adversarial Defense
Weakly-Supervised Object Localization
Adversarial Robustness
Image Deblurring
Partial Domain Adaptation
Network Pruning
Knowledge Distillation
Model Compression
Classification with Binary Weight Network
Image Compressed Sensing
Non-exemplar-based Class Incremental Learning
Sparse Learning
Robust classification
Classification with Binary Neural Network
Unconditional Image Generation
Data Free Quantization
Open-World Semi-Supervised Learning
Neural Network Compression
Variational Inference
Parameter Prediction
Linear-Probe Classification
Image Colorization
Biologically-plausible Training
Image Classification with Differential Privacy
CW Attack Detection