Girish, SharathIn this thesis, we examine the efficiency of deep networks and data, both of which are widely used in various computer vision/AI applications and are ubiquitous in today's information age. As deep networks continue to grow exponentially in size, improving their efficiency in terms of size and computation becomes necessary for deploying across various mobile/small devices with hardware constraints. Data efficiency is also equivalently important due to the memory and network speed bottlenecks when transmitting and storing data which is also being created and transmitted at an exponential rate. In this work, we explore in detail, various approaches to improve the efficiency of deep networks, as well as perform compression of various forms of data content. Efficiency of deep networks involves two major aspects; size, or the memory required to store deep networks on disk, and computation, or the number of operations/time taken to execute the network. The first work analyzes sparsity for computation reduction in the context of vision tasks which involve a large pretraining stage followed by downstream task finetuning. We show that task specific sparse subnetworks are more efficient than generalized sparse subnetworks which are more dense and do not transfer very well. We analyze several behaviors of training sparse networks for various vision tasks. While efficient, this sparsity theoretically focuses on only computation reduction and requires dedicated hardware for practical deployment. We therefore develop a framework for simultaneously reducing size and computation by utilizing a latent quantization-framework along with regularization losses. We compress convolutional networks by more than an order of magnitude in size while maintaining accuracy and speeding up inference without dedicated hardware. Data can take different forms such as audio, language, image, or video. We develop approaches for improving the compression and efficiency of various forms of visual data which take up the bulk of global network traffic as well as storage. This consists of 2D images or videos and, more recently, their 3D equivalents of static/dynamic scenes which are becoming popular for immersive AR/VR applications, scene understanding, 3D-aware generative modeling, and so on. To achieve data compression, we utilize Implicit Neural Representations (INRs) which represent data signals in terms of deep network weights. We transform the problem of data compression into network compression, thereby learning efficient data representations. We first develop an algorithm for compression of 2D videos via autoregressive INRs whose weights are compressed by utilizing the latent-quantization framework. We then focus on learning a general-purpose INR which can compress different forms of data such as 2D images/videos and can potentially be extended to the audio or language domain as well. This can be extended to compression of 3D objects and scenes as well. Finally, while INRs can represent 3D information, they are slow to train and render which are important for various real-time 3D applications. We utilize 3D Gaussian Splatting (3D-GS), a form of explicit representation for 3D scenes or objects. 3D-GS is quite fast to train and render, but consume large amounts of memory and are especially inefficient for modeling dynamic scenes or 3D videos. We first develop a framework for efficiently training and compressing 3D-GS for static scenes. We achieve large reductions in storage memory, runtime memory, training and rendering time costs while maintaining high reconstruction quality. Next, we extend to dynamic scenes or 3D videos, developing an online streamable framework for 3D-GS. We learn per-frame 3D-GS and learn/transmit only the residuals for 3D-GS attributes achieving large reductions in per-frame storage memory for online streamable 3D-GS while also reducing training time costs and maintaining high rendering speeds and reconstruction quality.enDissertationArtificial intelligenceComputer science3D representationCompressionDataDeep NetworksEfficient AIImplicit Neural Representations