You signed in with another tab or window. During generation, we only sample a subset of $S$ diffusion steps $\{\tau_1, \dots, \tau_S\}$ and the inference process becomes: While all the models are trained with $T=1000$ diffusion steps in the experiments, they observed that DDIM ($\eta=0$) can produce the best quality samples when $S$ is small, while DDPM ($\eta=1$) performs much worse on small $S$. Diffusion models can be seen as latent variable models. L_t &= D_\text{KL}(q(\mathbf{x}_t \vert \mathbf{x}_{t+1}, \mathbf{x}_0) \parallel p_\theta(\mathbf{x}_t \vert\mathbf{x}_{t+1})) \text{ for }1 \leq t \leq T-1 \\ $$, $$ This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Video Understanding / Activity Recognition, GUI Application / Large Scale Tracking / Animals, MOTS: Multi-Object Tracking and Segmentation, 3D Traffic Scene Understanding from Movable Platforms, LOST : Longterm Observation of Scenes with Tracks, PathTrack: Fast Trajectory Annotation with Path Supervision, TAO: A Large-Scale Benchmark for Tracking Any Object, Edinburgh office monitoring video dataset, UAVDT - The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking, TUB Multi-Object and Multi-Camera Tracking Dataset, CTMC: Cell Tracking with Mitosis Detection Dataset Challenge, TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild, LaSOT: Large-scale Single Object Tracking, Need for speed: A benchmark for higher frame rate object tracking, Long-term Tracking in the Wild A Benchmark, UAV123: A benchmark and simulator for UAV tracking, Sim4CV A Photo-Realistic Simulator for Computer Vision Applications, CDTB: A Color and Depth Visual Object Tracking and Benchmark, Temple Color 128 - Color Tracking Benchmark, AVA: A Video Dataset of Atomic Visual Action, A Large-Scale Dataset for Vehicle Re-Identification in the Wild, Object Detection-based annotations for some frames of the VIRAT dataset, MIO-TCD: A new benchmark dataset for vehicle classification and localization, Wildlife Image and Localization Dataset (species and bounding box labels), Gold Standard Snapshot Serengeti Bounding Box Coordinates, Semantic Boundaries Dataset and Benchmark, UC Berkeley Computer Vision Group - Contour Detection and Image Segmentation, DAVIS: Densely Annotated VIdeo Segmentation, ImageNet Large Scale Visual Recognition Competition 2012, Trajnet++ (A Trajectory Forecasting Challenge), OpenMMLab Video Perception Toolbox. Improved techniques for training score-based generative models. NeuriPS 2020. Usually, we can afford a larger update step when the sample gets noisier, so $\beta_1 < \beta_2 < \dots < \beta_T$ and therefore $\bar{\alpha}_1 > \dots > \bar{\alpha}_T$. I use DavidRM Journal for managing my research data for its excellent hierarchical organization, cross-linking and tagging capabilities. Ultimate-Awesome-Transformer-Attention . &= - \mathbb{E}_{q(\mathbf{x}_0)} \log \Big( \mathbb{E}_{q(\mathbf{x}_{1:T} \vert \mathbf{x}_0)} \frac{p_\theta(\mathbf{x}_{0:T})}{q(\mathbf{x}_{1:T} \vert \mathbf{x}_{0})} \Big) \\ \begin{aligned} A subjective human evaluation (mean opinion score, or MOS) on the LJ Speech, a single speaker dataset, shows that our method outperforms the best publicly available TTS systems and achieves a MOS comparable to ground truth. Generative ModelsGenerative Adversarial NetworkGANGANGAN45 &= \exp\Big( -\frac{1}{2} \big( \color{red}{(\frac{\alpha_t}{\beta_t} + \frac{1}{1 - \bar{\alpha}_{t-1}})} \mathbf{x}_{t-1}^2 - \color{blue}{(\frac{2\sqrt{\alpha_t}}{\beta_t} \mathbf{x}_t + \frac{2\sqrt{\bar{\alpha}_{t-1}}}{1 - \bar{\alpha}_{t-1}} \mathbf{x}_0)} \mathbf{x}_{t-1} \color{black}{ + C(\mathbf{x}_t, \mathbf{x}_0) \big) \Big)} 23 (2022): 47-1. ** Update note: Thanks to Rishikesh (), our interactive TTS demo is now available on Colab Notebook. &= \mathbb{E}_q \Big[ -\log p_\theta(\mathbf{x}_T) + \sum_{t=2}^T \log \frac{q(\mathbf{x}_{t-1} \vert \mathbf{x}_t, \mathbf{x}_0)}{p_\theta(\mathbf{x}_{t-1} \vert\mathbf{x}_t)} + \log\frac{q(\mathbf{x}_T \vert \mathbf{x}_0)}{q(\mathbf{x}_1 \vert \mathbf{x}_0)} + \log \frac{q(\mathbf{x}_1 \vert \mathbf{x}_0)}{p_\theta(\mathbf{x}_0 \vert \mathbf{x}_1)} \Big]\\ So far, Ive written about three types of generative models, GAN, VAE, and Flow-based models. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Use Git or checkout with SVN using the web URL. via a Gaussian or Laplace), but they cannot easily describe the structure in rich datasets. Flexible models can fit arbitrary structures in data, but evaluating, training, or sampling from these models is usually expensive. &= \sqrt{\bar{\alpha}_{t-1}}\mathbf{x}_0 + \sqrt{1 - \bar{\alpha}_{t-1} - \sigma_t^2} \frac{\mathbf{x}_t - \sqrt{\bar{\alpha}_t}\mathbf{x}_0}{\sqrt{1 - \bar{\alpha}_t}} + \sigma_t\boldsymbol{\epsilon} \\ \varphi_i(\mathbf{z}_i) \in \mathbb{R}^{N \times d^i_\epsilon},\; Unfortunately, we cannot easily estimate $q(\mathbf{x}_{t-1} \vert \mathbf{x}_t)$ because it needs to use the entire dataset and therefore we need to learn a model $p_\theta$ to approximate these conditional probabilities in order to run the reverse diffusion process. &= \frac{\sqrt{\alpha_t}(1 - \bar{\alpha}_{t-1})}{1 - \bar{\alpha}_t} \mathbf{x}_t + \frac{\sqrt{\bar{\alpha}_{t-1}}\beta_t}{1 - \bar{\alpha}_t} \mathbf{x}_0\\ Several diffusion-based generative models have been proposed with similar ideas underneath, including diffusion probabilistic models (Sohl-Dickstein et al., 2015), noise-conditioned score network (NCSN; Yang & Ermon, 2019), and denoising diffusion probabilistic models (DDPM; Ho et al. [pdf] The data sample $\mathbf{x}_0$ gradually loses its distinguishable features as the step $t$ becomes larger. [10] Yang Song, et al. A tag already exists with the provided branch name. A prior model $P(\mathbf{c}^i \vert y)$: outputs CLIP image embedding $\mathbf{c}^i$ given the text $y$. CGAN 2. They have shown great success in generating high-quality samples, but each has some limitations of its own. [1] Jascha Sohl-Dickstein et al. Multi-prediction deep boltzmann machines. &\text{and } IDOT; UA-DETRAC Benchmark Suite; GRAM Road-Traffic Monitoring; Ko-PER Intersection Dataset; TRANCOS; Urban Tracker \begin{aligned} \end{aligned} DDIM has the same marginal noise distribution but deterministically maps noise back to the original data samples. In our recent paper, we propose VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech.. Several recent end-to-end text-to-speech (TTS) models enabling single-stage training and A decoder $P(\mathbf{x} \vert \mathbf{c}^i, [y])$: generates the image $\mathbf{x}$ given CLIP image embedding $\mathbf{c}^i$ and optionally the original text $y$. To add the dependency, they constructed a hybrid objective $L_\text{hybrid} = L_\text{simple} + \lambda L_\text{VLB}$ where $\lambda=0.001$ is small and stop gradient on $\boldsymbol{\mu}_\theta$ in the $L_\text{VLB}$ term such that $L_\text{VLB}$ only guides the learning of $\boldsymbol{\Sigma}_\theta$. [notes], A Simple Baseline for Multi-Object Tracking Precisely, a conditional diffusion model $p_\theta(\mathbf{x} \vert y)$ is trained on paired data $(\mathbf{x}, y)$, where the conditioning information $y$ gets discarded periodically at random such that the model knows how to generate images unconditionally as well, i.e. \bar{\boldsymbol{\epsilon}}_\theta(\mathbf{x}_t, t) = \boldsymbol{\epsilon}_\theta(x_t, t) - \sqrt{1 - \bar{\alpha}_t} \; w \nabla_{\mathbf{x}_t} \log f_\phi(y \vert \mathbf{x}_t) Latent diffusion model (LDM; Rombach & Blattmann, et al. Given a pretrained CLIP model $\mathbf{c}$ and paired training data for the diffusion model, $(\mathbf{x}, y)$, where $x$ is an image and $y$ is the corresponding caption, we can compute the CLIP text and image embedding, $\mathbf{c}^t(y)$ and $\mathbf{c}^i(\mathbf{x})$, respectively. Cons: Diffusion models rely on a long Markov chain of diffusion steps to generate samples, so it can be quite expensive in terms of time and compute. They define a Markov chain of diffusion steps to slowly add random noise to data and then learn to reverse the diffusion process to construct desired data samples from the noise. \begin{aligned} L_\text{VLB} unCLIP follows a two-stage image generation process: Instead of CLIP model, Imagen (Saharia et al. An LSTM Autoencoder is an implementation of an autoencoder for sequence data using an Encoder-Decoder LSTM architecture. If nothing happens, download GitHub Desktop and try again. \end{aligned} &\text{where }\mathbf{Q} = \mathbf{W}^{(i)}_Q \cdot \varphi_i(\mathbf{z}_i),\; [arxiv] \begin{aligned} Two thresholding strategies are introduced: Imagen modifies several designs in U-net to make it efficient U-Net. The encoding is validated and refined by attempting to regenerate the input from the encoding. $$, $$ With the uncertainty modeling over latent variables and the stochastic duration predictor, our method expresses the natural one-to-many relationship in which a text input can be spoken in multiple ways with different pitches and rhythms. High-Resolution Image Synthesis with Latent Diffusion Models." 2022) uses a pre-trained large LM (i.e. arxiv Preprint arxiv:2204.06125 (2022). 2016. &= \mathbb{E}_{q(\mathbf{x}_{0:T})} \Big[ \log\frac{q(\mathbf{x}_{1:T}\vert\mathbf{x}_0)}{p_\theta(\mathbf{x}_{0:T})} \Big] \\ What are diffusion models? In our recent paper, we propose VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech. 2022) runs the diffusion process in the latent space instead of pixel space, making training cost lower and inference speed faster. Face images generated with a Variational Autoencoder (source: Wojciech Mormul on Github). End-to-end object detection with Transformers, Deep Learning for Object Detection: A Comprehensive Review, Review of Deep Learning Algorithms for Object Detection, A Simple Guide to the Versions of the Inception Network, R-CNN, Fast R-CNN, Faster R-CNN, YOLO - Object Detection Algorithms, A gentle guide to deep learning object detection, YOLOYou only look once, real time object detection explained, Understanding Feature Pyramid Networks for object detection (FPN), Fast object detection with SqueezeDet on Keras, How Microsoft Does Video Object Detection - Unifying the Best Techniques in Video Object Detection Architectures in a Single Model, Splash of Color: Instance Segmentation with Mask R-CNN and TensorFlow, Analyzing The Papers Behind Facebook's Computer Vision Approach, Review: MNC Multi-task Network Cascade, Winner in 2015 COCO Segmentation, Review: FCIS Winner in 2016 COCO Segmentation, Review: InstanceFCN Instance-Sensitive Score Maps, Handling imbalanced datasets in machine learning, How to Handle Imbalanced Classes in Machine Learning, 10 Techniques to deal with Imbalanced Classes in Machine Learning, The Unreasonable Effectiveness of Recurrent Neural Networks, Deep Reinforcement Learning: Pong from Pixels, Applied Deep Learning - Part 3: Autoencoders, A Gentle Introduction to LSTM Autoencoders, Variational Autoencoders with Tensorflow Probability Layers, Jay Alammar : Visualizing machine learning one concept at a time, Inside Machine Learning: Deep-dive articles about machine learning, cloud, and data. April 13, 2022 Variational Lossy Autoencoder. Using a black box likelihood function (numpy) Automatic autoencoding variational Bayes for latent dirichlet allocation with PyMC3 Empirical Approximation overview. Denoising diffusion probabilistic models. arxiv Preprint arxiv:2006.11239 (2020). To make it scalable with high-dimensional data in the deep learning setting, they proposed to use either denoising score matching (Vincent, 2011) or sliced score matching (use random projections; Song et al., 2019). Rick.King: [pdf] Recall that $q(\mathbf{x}_t \vert \mathbf{x}_0) \sim \mathcal{N}(\sqrt{\bar{\alpha}_t} \mathbf{x}_0, (1 - \bar{\alpha}_t)\mathbf{I})$ and therefore. Generative Adversarial NetsGoodfellow[5]GDxPgG Pz (z) G(z; g ) DD(x; d ) GDDG log(1 D(G(z))GDlogD(X) + log(1 D(G(z)))minimax two-player game: CGANGANy, y,,Figure 1y,,GAN,p(z)yGANtwo-player minimax game , MNISTone-hotGAN100yone hotzy(2001000),,sigmoid(784)28*28 784yone hot, automated tagging of imagesGANtag-vector MIR Flickr 25,000 dataset :skip-gram,200 / 100=>500 4096=>2000 200, (200) / : 500; 1200 ???y,?? The perceptual compression process relies on an autoencoder model. Recall that we need to learn a neural network to approximate the conditioned probability distributions in the reverse diffusion process, $p_\theta(\mathbf{x}_{t-1} \vert \mathbf{x}_t) = \mathcal{N}(\mathbf{x}_{t-1}; \boldsymbol{\mu}_\theta(\mathbf{x}_t, t), \boldsymbol{\Sigma}_\theta(\mathbf{x}_t, t))$. &= \mathbb{E}_{q(\mathbf{x}_{0:T})}\Big[\log \frac{q(\mathbf{x}_{1:T} \vert \mathbf{x}_{0})}{p_\theta(\mathbf{x}_{0:T})} \Big] = L_\text{VLB} [4] Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks[J]. \text{Thus }\mathbf{x}_{t-1} &= \mathcal{N}(\mathbf{x}_{t-1}; \frac{1}{\sqrt{\alpha_t}} \Big( \mathbf{x}_t - \frac{1 - \alpha_t}{\sqrt{1 - \bar{\alpha}_t}} \boldsymbol{\epsilon}_\theta(\mathbf{x}_t, t) \Big), \boldsymbol{\Sigma}_\theta(\mathbf{x}_t, t)) The design is equivalent to fuse representation of different modality into the model with cross-attention mechanism. Rather than use digits, were going to use the Fashion MNIST dataset, which has 28-by-28 grayscale images of different clothing items 5. \tau_\theta(y) \in \mathbb{R}^{M \times d_\tau} Recall that $\nabla_{\mathbf{x}_t} \log q(\mathbf{x}_t) = - \frac{1}{\sqrt{1 - \bar{\alpha}_t}} \boldsymbol{\epsilon}_\theta(\mathbf{x}_t, t)$ and we can write the score function for the joint distribution $q(\mathbf{x}_t, y)$ as following. \begin{aligned} A score network $\mathbf{s}_\theta: \mathbb{R}^D \to \mathbb{R}^D$ is trained to estimate it, $\mathbf{s}_\theta(\mathbf{x}) \approx \nabla_{\mathbf{x}} \log q(\mathbf{x})$. 2020: For example, it takes around 20 hours to sample 50k images of size 32 32 from a DDPM, but less than a minute to do so from a GAN on an Nvidia 2080 Ti GPU.. $$, $$ &= \frac{\sqrt{\alpha_t}(1 - \bar{\alpha}_{t-1})}{1 - \bar{\alpha}_t} \mathbf{x}_t + \frac{\sqrt{\bar{\alpha}_{t-1}}\beta_t}{1 - \bar{\alpha}_t} \frac{1}{\sqrt{\bar{\alpha}_t}}(\mathbf{x}_t - \sqrt{1 - \bar{\alpha}_t}\boldsymbol{\epsilon}_t) \\ [ax1909] k ? [15] Rombach & Blattmann, et al. \end{aligned} &= - \mathbb{E}_{q(\mathbf{x}_0)} \log \Big( \int q(\mathbf{x}_{1:T} \vert \mathbf{x}_0) \frac{p_\theta(\mathbf{x}_{0:T})}{q(\mathbf{x}_{1:T} \vert \mathbf{x}_{0})} d\mathbf{x}_{1:T} \Big) \\ 2., such a setup is very similar to VAE and thus we can use the variational lower bound to optimize the negative log-likelihood. ICML 2016. [10] Diederik P. Kingma, et al. Implementation of Recurrent Neural Networks for future trajectory prediction of pedestrians. [code]. A conditional variational autoencoder. It needs Jounal 8 and can be imported using File -> Import -> Sync from The Journal Export File. $$, $$ What is the Multi-Object Tracking (MOT) system? &\leq - \log p_\theta(\mathbf{x}_0) + D_\text{KL}(q(\mathbf{x}_{1:T}\vert\mathbf{x}_0) \| p_\theta(\mathbf{x}_{1:T}\vert\mathbf{x}_0) ) \\ Computer Science, 2014:2672-2680. VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech Jaehyeon Kim, Jungil Kong, and Juhee Son. 2020). [ax1708/iccv17] $$, $$ \end{aligned} Classifier-Free Diffusion Guidance." If nothing happens, download Xcode and try again. ,\quad\text{where } Noise conditioning augmentation between pipeline models is crucial to the final image quality, which is to apply strong data augmentation to the conditioning input $\mathbf{z}$ of each super-resolution model $p_\theta(\mathbf{x} \vert \mathbf{z})$. View source on GitHub: Download notebook: This notebook demonstrates how to train a Variational Autoencoder (VAE) (1, which takes as input an observation and outputs a set of parameters for specifying the conditional distribution of the latent representation \(z\). $$, $$ GDDG log(1 D(G(z))GDlogD(X) + log(1 D(G(z)))minimax two-player game: MNISTone-hotGAN100yone hotzy(2001000),,sigmoid(784)28*28, 784yone hot, automated tagging of imagesGANtag-vector MIR Flickr 25,000 dataset :skip-gram,200, k ? Have consistency property since the generative process is deterministic, meaning that multiple samples conditioned on the same latent variable should have similar high-level features. -VAE: Learning Basic Visual Concepts with a Constrained Variational Framework, Learning Efficient Convolutional Networks Through Network Slimming, OpenMMLab Image Classification Toolbox and Benchmark, Asynchronous Methods for Deep Reinforcement Learning, ByLabel: A Boundary Based Semi-Automatic Image Annotation Tool, Visual Object Tagging Tool: An electron app for building end to end Object Detection Models from Images and Videos, labelme : Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation), VATIC - Video Annotation Tool from Irvine, California). Computer Science, 2014:2672-2680. [code], [7] Alex Nichol & Prafulla Dhariwal. = \mathcal{N}(\mathbf{x}_{\tau_{i-1}}; \sqrt{\bar{\alpha}_{t-1}}\mathbf{x}_0 + \sqrt{1 - \bar{\alpha}_{t-1} - \sigma_t^2} \frac{\mathbf{x}_{\tau_i} - \sqrt{\bar{\alpha}_t}\mathbf{x}_0}{\sqrt{1 - \bar{\alpha}_t}}, \sigma_t^2 \mathbf{I}) A nice property of the above process is that we can sample $\mathbf{x}_t$ at any arbitrary time step $t$ in a closed form using reparameterization trick. The unCLIP learns two models in parallel: These two models enable conditional generation, because. Collection of generative models, e.g. A diffusion or autoregressive prior $P(\mathbf{c}^i \vert y)$ processes this CLIP text embedding to construct an image prior and then a diffusion decoder $P(\mathbf{x} \vert \mathbf{c}^i, [y])$ generates an image, conditioned on the prior. 2020 models $L_0$ using a separate discrete decoder derived from $\mathcal{N}(\mathbf{x}_0; \boldsymbol{\mu}_\theta(\mathbf{x}_1, 1), \boldsymbol{\Sigma}_\theta(\mathbf{x}_1, 1))$. Shift model parameters from high resolution blocks to low resolution by adding more residual locks for the lower resolutions; Scale the skip connections by $1/\sqrt{2}$. Contribute to zziz/pwc development by creating an account on GitHub. &= \mathbb{E}_q \Big[ -\log p_\theta(\mathbf{x}_T) + \sum_{t=2}^T \log \frac{q(\mathbf{x}_t\vert\mathbf{x}_{t-1})}{p_\theta(\mathbf{x}_{t-1} \vert\mathbf{x}_t)} + \log\frac{q(\mathbf{x}_1 \vert \mathbf{x}_0)}{p_\theta(\mathbf{x}_0 \vert \mathbf{x}_1)} \Big] \\ \end{aligned} 9000 classes! $$, $$ arXiv preprint arXiv:1511.06434, 2015. GANGANGANformulate p(x)samplingGAN pixel GAN GANGANConditional Generative Adversarial NetsCGANMirza M, Osindero S. ConditionalGANDGyconditional variable yyy[2]modalityyCGAN GAN ,[3,4]Mehdi Mirza et al. Static thresholding: clip $\mathbf{x}$ prediction to $[-1, 1]$. $$, $$ \begin{aligned} &= \mathbb{E}_{q(\mathbf{x}_{0:T})} \Big[ \log \frac{q(\mathbf{x}_{1:T}\vert\mathbf{x}_0)}{p_\theta(\mathbf{x}_{0:T})} \Big] \geq - \mathbb{E}_{q(\mathbf{x}_0)} \log p_\theta(\mathbf{x}_0) Convolutional variational autoencoder with PyMC3 and Keras. &= \mathbb{E}_{\mathbf{x}_0, \boldsymbol{\epsilon}} \Big[\frac{ (1 - \alpha_t)^2 }{2 \alpha_t (1 - \bar{\alpha}_t) \| \boldsymbol{\Sigma}_\theta \|^2_2} \|\boldsymbol{\epsilon}_t - \boldsymbol{\epsilon}_\theta(\mathbf{x}_t, t)\|^2 \Big] \\ \mathbf{x}_t = \mathbf{x}_{t-1} + \frac{\delta}{2} \nabla_\mathbf{x} \log q(\mathbf{x}_{t-1}) + \sqrt{\delta} \boldsymbol{\epsilon}_t There is a general trend that larger model size can lead to better image quality and text-image alignment. ICML 2015. $$, $$ Nichol & Dhariwal (2021) proposed several improvement techniques to help diffusion models to obtain lower NLL. To explicit incorporate class information into the diffusion process, Dhariwal & Nichol (2021) trained a classifier $f_\phi(y \vert \mathbf{x}_t, t)$ on noisy image $\mathbf{x}_t$ and use gradients $\nabla_\mathbf{x} \log f_\phi(y \vert \mathbf{x}_t)$ to guide the diffusion sampling process toward the conditioning information $y$ (e.g. 2022), explored both guiding strategies, CLIP guidance and classifier-free guidance, and found that the latter is more preferred. The new sampling schedule for generation is $\{\tau_1, \dots, \tau_S\}$ where $\tau_1 < \tau_2 < \dots <\tau_S \in [1, T]$ and $S < T$. The conditioning noise helps reduce compounding error in the pipeline setup. [Updated on 2022-08-27: Added classifier-free guidance, GLIDE, unCLIP and Imagen. language model. [notes], Exploit the Connectivity: Multi-Object Tracking with TrackletNet In a previous post, published in January of this year, we discussed in depth Generative Adversarial Networks (GANs) and showed, in particular, how adversarial training can oppose two networks, a generator and a discriminator, to push both of them to improve iteration after Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding." Variational. $$, $$ \bar{\boldsymbol{\epsilon}}_\theta(\mathbf{x}_t, t, y) ICML 2022. The choice of the scheduling function can be arbitrary, as long as it provides a near-linear drop in the middle of the training process and subtle changes around $t=0$ and $t=T$. &= \mathcal{N}(\mathbf{x}_{t-1}; \sqrt{\bar{\alpha}_{t-1}}\mathbf{x}_0 + \sqrt{1 - \bar{\alpha}_{t-1} - \sigma_t^2} \frac{\mathbf{x}_t - \sqrt{\bar{\alpha}_t}\mathbf{x}_0}{\sqrt{1 - \bar{\alpha}_t}}, \sigma_t^2 \mathbf{I}) \end{aligned} Empirically they observed that $L_\text{VLB}$ is pretty challenging to optimize likely due to noisy gradients, so they proposed to use a time-averaging smoothed version of $L_\text{VLB}$ with importance sampling. So far, Ive written about three types of generative models, GAN, &= \mathbb{E}_q \Big[ -\log p_\theta(\mathbf{x}_T) + \sum_{t=2}^T \log \Big( \frac{q(\mathbf{x}_{t-1} \vert \mathbf{x}_t, \mathbf{x}_0)}{p_\theta(\mathbf{x}_{t-1} \vert\mathbf{x}_t)}\cdot \frac{q(\mathbf{x}_t \vert \mathbf{x}_0)}{q(\mathbf{x}_{t-1}\vert\mathbf{x}_0)} \Big) + \log \frac{q(\mathbf{x}_1 \vert \mathbf{x}_0)}{p_\theta(\mathbf{x}_0 \vert \mathbf{x}_1)} \Big] \\ \begin{aligned} We also propose a stochastic duration predictor to synthesize speech with diverse rhythms from input text. Following the standard Gaussian density function, the mean and variance can be parameterized as follows (recall that $\alpha_t = 1 - \beta_t$ and $\bar{\alpha}_t = \prod_{i=1}^T \alpha_i$): Thanks to the nice property, we can represent $\mathbf{x}_0 = \frac{1}{\sqrt{\bar{\alpha}_t}}(\mathbf{x}_t - \sqrt{1 - \bar{\alpha}_t}\boldsymbol{\epsilon}_t)$ and plug it into the above equation and obtain: As demonstrated in Fig. For another approach, lets rewrite $q_\sigma(\mathbf{x}_{t-1} \vert \mathbf{x}_t, \mathbf{x}_0)$ to be parameterized by a desired standard deviation $\sigma_t$ according to the nice property: Recall that in $q(\mathbf{x}_{t-1} \vert \mathbf{x}_t, \mathbf{x}_0) = \mathcal{N}(\mathbf{x}_{t-1}; \tilde{\boldsymbol{\mu}}(\mathbf{x}_t, \mathbf{x}_0), \tilde{\beta}_t \mathbf{I})$, therefore we have: Let $\sigma_t^2 = \eta \cdot \tilde{\beta}_t$ such that we can adjust $\eta \in \mathbb{R}^+$ as a hyperparameter to control the sampling stochasticity. [Updated on 2022-08-27: Added classifier-free guidance, GLIDE, unCLIP and Imagen. [9] Aaron van den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu. At training time, the number whose image is being fed in is provided to the encoder and decoder. $$, $$ (2021) proposed to use a pipeline of multiple diffusion models at increasing resolutions. &= (\frac{\sqrt{\alpha_t}}{\beta_t} \mathbf{x}_t + \frac{\sqrt{\bar{\alpha}_{t-1} }}{1 - \bar{\alpha}_{t-1}} \mathbf{x}_0) \color{green}{\frac{1 - \bar{\alpha}_{t-1}}{1 - \bar{\alpha}_t} \cdot \beta_t} \\ Then an decoder $\mathcal{D}$ reconstructs the images from the latent vector, $\tilde{\mathbf{x}} = \mathcal{D}(\mathbf{z})$. $$, $$ Our method adopts variational inference augmented with normalizing flows and an adversarial training process, which improves the expressive power of generative modeling. Say we want to minimize the cross entropy as the learning objective. [ax1811/mm19] Collection of papers, datasets, code and other resources for object detection and tracking using deep learning. \begin{aligned} Using CLIP latent space enables zero-shot image manipulation via text. &= \sqrt{\bar{\alpha}_t}\mathbf{x}_0 + \sqrt{1 - \bar{\alpha}_t}\boldsymbol{\epsilon} \\ To control the strength of the classifier guidance, we can add a weight $w$ to the delta part. Let $\alpha_t = 1 - \beta_t$ and $\bar{\alpha}_t = \prod_{i=1}^t \alpha_i$: (*) Recall that when we merge two Gaussians with different variance, $\mathcal{N}(\mathbf{0}, \sigma_1^2\mathbf{I})$ and $\mathcal{N}(\mathbf{0}, \sigma_2^2\mathbf{I})$, the new distribution is $\mathcal{N}(\mathbf{0}, (\sigma_1^2 + \sigma_2^2)\mathbf{I})$. Song & Ermon (2019) improved it by perturbing the data with the noise of different levels and train a noise-conditioned score network to jointly estimate the scores of all the perturbed data at different noise levels. Each type of conditioning information is paired with a domain-specific encoder $\tau_\theta$ to project the conditioning input $y$ to an intermediate representation that can be mapped into cross-attention component, $\tau_\theta(y) \in \mathbb{R}^{M \times d_\tau}$: While training generative models on images with conditioning information such as ImageNet dataset, it is common to generate samples conditioned on class labels or a piece of descriptive text. \mathbf{x}_{t-1} Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation, Few-shot Segmentation Propagation with Guided Networks, Deep Extreme Cut (DEXTR): From Extreme Points to Object Segmentation, FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation, OpenMMLab Semantic Segmentation Toolbox and Benchmark, PraNet: Parallel Reverse Attention Network for Polyp Segmentation, PHarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPS, Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation, Improving Semantic Segmentation via Video Prediction and Label Relaxation, PReMVOS: Proposal-generation, Refinement and Merging for Video Object Segmentation, MaskTrackRCNN for video instance segmentation, Video Instance Segmentation using Inter-Frame Communication Transformers, SeqFormer: Sequential Transformer for Video Instance Segmentation, VITA: Video Instance Segmentation via Object Token Association, Self-Supervised Learning via Conditional Motion Propagation, A Neural Temporal Model for Human Motion Prediction, Learning Trajectory Dependencies for Human Motion Prediction, Structural-RNN: Deep Learning on Spatio-Temporal Graphs, A Keras multi-input multi-output LSTM-based RNN for object trajectory forecasting, Transformer Networks for Trajectory Forecasting, Regularizing neural networks for future trajectory prediction via IRL framework, Peeking into the Future: Predicting Future Person Activities and Locations in Videos, DAG-Net: Double Attentive Graph Neural Network for Trajectory Forecasting, MCENET: Multi-Context Encoder Network for Homogeneous Agent Trajectory Prediction in Mixed Traffic, Human Trajectory Prediction in Socially Interacting Crowds Using a CNN-based Architecture, A tool set for trajectory prediction, ready for pip install, RobustTP: End-to-End Trajectory Prediction for Heterogeneous Road-Agents in Dense Traffic with Noisy Sensor Inputs, The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction, Overcoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future Prediction, Adversarial Loss for Human Trajectory Prediction, Social GAN: SSocially Acceptable Trajectories with Generative Adversarial Networks, Forecasting Trajectory and Behavior of Road-Agents Using Spectral Clustering in Graph-LSTMs, Study of attention mechanisms for trajectory prediction in Deep Learning.
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