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Found 10 papers, 5 papers with code
Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise
We observe that the generative behavior of diffusion models is not strongly dependent on the choice of image degradation, and in fact an entire family of generative models can be constructed by varying this choice.
NEFTune: Noisy Embeddings Improve Instruction Finetuning
We show that language model finetuning can be improved, sometimes dramatically, with a simple augmentation.
Universal Guidance for Diffusion Models
Typical diffusion models are trained to accept a particular form of conditioning, most commonly text, and cannot be conditioned on other modalities without retraining.
Active Learning at the ImageNet Scale
Active learning (AL) algorithms aim to identify an optimal subset of data for annotation, such that deep neural networks (DNN) can achieve better performance when trained on this labeled subset.
Deep Learning with a Rethinking Structure for Multi-label Classification
Extracting the hidden correlation is generally a challenging task.
Dynamic Principal Projection for Cost-Sensitive Online Multi-Label Classification
The foundation of CS-DPP is an online LSDR framework derived from a leading LSDR algorithm.
Can Active Learning Experience Be Transferred?
Empirical studies demonstrate that the learned experience not only is competitive with existing strategies on most single datasets, but also can be transferred across datasets to improve the performance on future learning tasks.
Deep Generative Models for Weakly-Supervised Multi-Label Classification
In order to train learning models for multi-label classification (MLC), it is typically desirable to have a large amount of fully annotated multi-label data.
WrapNet: Neural Net Inference with Ultra-Low-Resolution Arithmetic
Low-resolution neural networks represent both weights and activations with few bits, drastically reducing the multiplication complexity.
WrapNet: Neural Net Inference with Ultra-Low-Precision Arithmetic
Low-precision neural networks represent both weights and activations with few bits, drastically reducing the multiplication complexity.
