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Found 11 papers, 5 papers with code
Model compression as constrained optimization, with application to neural nets. Part II: quantization
We consider the problem of deep neural net compression by quantization: given a large, reference net, we want to quantize its real-valued weights using a codebook with $K$ entries so that the training loss of the quantized net is minimal.
âLearning-Compressionâ Algorithms for Neural Net Pruning
Pruning a neural net consists of removing weights without degrading its performance.
A Flexible, Extensible Software Framework for Neural Net Compression
We propose a software framework based on ideas of the Learning-Compression algorithm , that allows one to compress any neural network by different compression mechanisms (pruning, quantization, low-rank, etc.).
Structured Multi-Hashing for Model Compression
Despite the success of deep neural networks (DNNs), state-of-the-art models are too large to deploy on low-resource devices or common server configurations in which multiple models are held in memory.
A flexible, extensible software framework for model compression based on the LC algorithm
We propose a software framework based on the ideas of the Learning-Compression (LC) algorithm, that allows a user to compress a neural network or other machine learning model using different compression schemes with minimal effort.
Low-Rank Compression of Neural Nets: Learning the Rank of Each Layer
Neural net compression can be achieved by approximating each layer's weight matrix by a low-rank matrix.
Optimal Quantization Using Scaled Codebook
We study the problem of quantizing N sorted, scalar datapoints with a fixed codebook containing K entries that are allowed to be rescaled.
Model compression as constrained optimization, with application to neural nets. Part V: combining compressions
However, VGG nets can be better compressed by combining low-rank with a few floating point weights.
Faster Neural Net Inference via Forests of Sparse Oblique Decision Trees
We show that neural nets can be further compressed by replacing layers of it with a special type of decision forest.
E$^{2}$GAN: Efficient Training of Efficient GANs for Image-to-Image Translation
One highly promising direction for enabling flexible real-time on-device image editing is utilizing data distillation by leveraging large-scale text-to-image diffusion models, such as Stable Diffusion, to generate paired datasets used for training generative adversarial networks (GANs).
TextCraftor: Your Text Encoder Can be Image Quality Controller
Our findings reveal that, instead of replacing the CLIP text encoder used in Stable Diffusion with other large language models, we can enhance it through our proposed fine-tuning approach, TextCraftor, leading to substantial improvements in quantitative benchmarks and human assessments.
